U.S. patent application number 14/126866 was filed with the patent office on 2015-08-06 for mobile application management.
The applicant listed for this patent is MCAFEE, INC.. Invention is credited to Kaushal Kumar Dhruw, Kamlesh Halder, Venkatasubrahmanyam Krishnapur, Dattatraya Kulkarni, Venkata Krishnan Nagarajan, Srikanth Nalluri, Raja Sinha.
Application Number | 20150220734 14/126866 |
Document ID | / |
Family ID | 50488809 |
Filed Date | 2015-08-06 |
United States Patent
Application |
20150220734 |
Kind Code |
A1 |
Nalluri; Srikanth ; et
al. |
August 6, 2015 |
MOBILE APPLICATION MANAGEMENT
Abstract
Code of a particular application is analyzed against a semantic
model of a software development kit of a particular platform. The
semantic model associates a plurality of application behaviors with
respective application programming interface (API) calls of the
particular platform. A set of behaviors of the particular
application is identified based on the analysis of the code and a
particular one of the set of behaviors is identified as an
undesired behavior. The particular application can be automatically
modified to remediate the undesired behavior. The particular
application can be assigned to one of a plurality of device modes,
and access to the particular application on a user device can be
based on which of the plurality of device modes is active on the
user device.
Inventors: |
Nalluri; Srikanth;
(Bangalore, IN) ; Kulkarni; Dattatraya;
(Bangalore, IN) ; Sinha; Raja; (Bangalore, IN)
; Krishnapur; Venkatasubrahmanyam; (Bangalore, IN)
; Nagarajan; Venkata Krishnan; (Chennai, IN) ;
Dhruw; Kaushal Kumar; (Bilaspur, IN) ; Halder;
Kamlesh; (Bangalore, IN) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
MCAFEE, INC. |
Santa Clara |
CA |
US |
|
|
Family ID: |
50488809 |
Appl. No.: |
14/126866 |
Filed: |
October 18, 2013 |
PCT Filed: |
October 18, 2013 |
PCT NO: |
PCT/US2013/065799 |
371 Date: |
December 17, 2013 |
Current U.S.
Class: |
726/23 |
Current CPC
Class: |
G06F 21/6218 20130101;
G06F 21/51 20130101; G06F 2221/2105 20130101; G06F 2221/2111
20130101; G06F 2221/033 20130101; G06F 2221/2149 20130101; G06F
2221/2113 20130101; G06F 21/552 20130101; G06F 21/563 20130101;
G06F 8/436 20130101 |
International
Class: |
G06F 21/56 20060101
G06F021/56; G06F 21/55 20060101 G06F021/55 |
Foreign Application Data
Date |
Code |
Application Number |
Oct 19, 2012 |
IN |
1215/KOL/2012 |
Claims
1-74. (canceled)
75. At least one machine accessible storage medium having
instructions stored thereon, the instructions when executed on a
machine, cause the machine to: analyze code of a particular
application against a semantic model of a software development kit
of a particular platform, wherein the semantic model associates a
plurality of application behaviors with respective application
programming interface (API) calls of the particular platform;
identify, based on the analysis of the code, a set of behaviors of
the particular application; and identify that a particular one of
the set of behaviors is an undesired behavior.
76. The storage medium of claim 75, wherein identifying that the
particular behavior is an undesired behavior includes determining
that the one or more behaviors violate one or more rules.
77. The storage medium of claim 76, wherein the rules are
associated with a particular user.
78. The storage medium of claim 77, wherein at least a portion of
the rules include rules defined by the particular user.
79. The storage medium of claim 76, wherein the rules are
associated with a network service provider.
80. The storage medium of claim 75, wherein a user input identifies
that the particular behavior is undesired.
81. The storage medium of claim 80, wherein the user input is
received in connection with a user interface displaying human
readable descriptions of the identified set of behaviors.
82. The storage medium of claim 81, wherein the human readable
description is generated using a template for generating the
description and the semantic model.
83. The storage medium of claim 75, wherein the particular user
device is one of a smart phone and a tablet computing device.
84. A method comprising: analyzing code of a particular application
against a semantic model of a software development kit of a
particular platform, the semantic model associating a plurality of
application behaviors with respective application programming
interface (API) calls of the particular platform; identifying,
based on the analysis of the code, a set of behaviors of the
particular application; and identifying that a particular one of
the set of behaviors is an undesired behavior.
85. The method of claim 84, further comprising disassembling code
of the particular application into a control flow and generating a
model of application logic for the particular application based at
least in part on the semantic model.
86. The method of claim 85, wherein the model of application logic
is further based, at least in part, on ambient application
knowledge.
87. The method of claim 84, further comprising performing a
remediation action based on the identification that one or more of
the set of behaviors are undesired behaviors.
88. The method of claim 84, wherein the code of the particular
application is analyzed in connection with an attempt to implement
the particular application on a particular user device.
89. The method of claim 88, further comprising restricting
implementation of the particular application on the particular user
device based on identifying that one or more of the set of
behaviors are undesired behaviors.
90. The method of claim 89, wherein restricting implementation
includes blocking installation of the particular application on the
particular user device.
91. The method of claim 89, wherein restricting implementation
includes assigning the particular application to a device mode that
is to limit access to the particular application.
92. The method of claim 89, further comprising modifying code of
the particular application to remediate the undesired behavior.
93. A system comprising: at least one processor device; at least
one memory element; and an application behavioral analysis engine,
adapted when executed by the at least one processor device to:
analyze code of a particular application against a semantic model
of a software development kit of a particular platform, wherein the
semantic model associates a plurality of application behaviors with
respective application programming interface (API) calls of the
particular platform; identify, based on the analysis of the code, a
set of behaviors of the particular application; and identify that a
particular one of the set of behaviors is an undesired
behavior.
94. The system of claim 93, further comprising an application
healer engine to: identify a section of code of the particular
application corresponding to the particular behavior; and perform a
remediation action on the section of code to remediate the
particular behavior and generate a healed version of the particular
application.
95. The system of claim 93, further comprising a mode manager to:
activate a particular one of a plurality of modes defined for a
user device; and restrict access to the particular application in
accordance with the activated particular mode, wherein the
particular application is made accessible when another one of the
plurality of modes is activated.
96. The system of claim 93, further comprising a user device,
wherein the application behavioral analysis engine is to
communicate results of the analysis of the code to the user device
based on an attempt by the user device to install the particular
application on the user device.
97. A system comprising: means for analyzing code of a particular
application against a semantic model of a software development kit
of a particular platform, the semantic model associating a
plurality of application behaviors with respective application
programming interface (API) calls of the particular platform; means
for identifying, based on the analysis of the code, a set of
behaviors of the particular application; and means for identifying
that a particular one of the set of behaviors is an undesired
behavior.
Description
TECHNICAL FIELD
[0001] This disclosure relates in general to the field of computer
security and, more particularly, to security of mobile devices.
BACKGROUND
[0002] The distribution and use of mobile devices, such as smart
phones, PDAs, laptops, netbooks, and tablets have grown at a rapid
pace. Further, adoption of such devices is also expanding and
number overtaking that of desktop computers and feature phones in
some developed markets. The sophistication of the operating systems
and the hardware capabilities of mobile devices is also increasing
and, in some cases, outpacing the features sets and functionality
of traditional computers. For example, modem mobile devices can
possess such varied sensors and subsystems as location sensors like
global positioning systems (GPS), accelerometers, gyroscopes, near
field communication (NFC), etc. that are ordinarily not included on
traditional devices. Adding to this the always connected nature of
some mobile devices and the tendency for their owners to constantly
carry the devices, mobile devices have become attractive targets
for malware developers, hackers, and other malicious actors.
Further, "app stores" and other open marketplaces have enabled the
development of tens of thousands of applications (or "apps") that
have been developed for such devices, including device platforms
such as Google Android.TM., iOS.TM., Windows.TM., etc., with some
of these applications being of questionable quality and
purpose.
BRIEF DESCRIPTION OF THE DRAWINGS
[0003] FIG. 1 is a simplified schematic diagram of an example
system including an application management system in accordance
with one embodiment;
[0004] FIG. 2 is a simplified block diagram of an example system
including an example application manager and user device in
accordance with one embodiment;
[0005] FIG. 3 is a simplified block diagram representing analysis
and healing of an application for a user device in accordance with
one embodiment;
[0006] FIG. 4 is a simplified block diagram representing an example
behavioral assessment of an application in accordance with one
embodiment;
[0007] FIGS. 5A-5B are simplified representation of control flow
within example applications in accordance with some
embodiments;
[0008] FIG. 6 is a simplified block diagram representing example
subsystems accessible to an example user device in accordance with
some embodiments;
[0009] FIG. 7 is a simplified block diagram representing use of
rules to determine application behaviors in accordance with some
embodiments;
[0010] FIG. 8 is a simplified flow diagram representing assessment
of application behaviors and healing of undesired behaviors in
accordance with one embodiment;
[0011] FIG. 9 is a simplified flow diagram representing decisions
made in connection with the management and remediation of
applications determined to include undesirable behaviors based on
behavioral analyses of the applications in accordance with one
embodiment;
[0012] FIG. 10 is a simplified flow diagram representing an example
healing of an application in accordance with one embodiment;
[0013] FIG. 11 is a simplified block diagram representing an
example healing of an application in accordance with one
embodiment;
[0014] FIGS. 12A-12E represent examples of detection and
remediation of undesired behaviors of an application in accordance
with some embodiments;
[0015] FIG. 13 is a simplified flow diagram representing an example
healing of an application in accordance with one embodiment;
[0016] FIGS. 14A-14B are simplified block diagram representing
features of an example mode manager in accordance with some
embodiments;
[0017] FIGS. 15A-15B represent portions of example algorithms for
managing modes in a user device in accordance with some
embodiments;
[0018] FIG. 16 is a simplified block diagram for sharing device
modes between devices in accordance with one embodiment;
[0019] FIG. 17 is a simplified flow diagram illustrating use of
context in managing modes of a device in accordance with one
embodiment;
[0020] FIG. 18 is a simplified flow diagram illustrating remote
provisioning and/or activation of modes on a user device in
accordance with some embodiments;
[0021] FIG. 19 is a simplified block diagram representing
application information collected in accordance with some
embodiments;
[0022] FIGS. 20A-20D are screenshots of example user interfaces
provided in connection with mode management of a user device in
accordance with some embodiments;
[0023] FIGS. 21A-21C are flowcharts representing example operations
involving an example application management system in accordance
with some embodiments.
[0024] Like reference numbers and designations in the various
drawings indicate like elements.
DETAILED DESCRIPTION
[0025] FIG. 1 illustrates an example system 100 including, for
instance, an example application management server 105, and one or
more mobile user devices 110, 115, 120, 125, such as smart phones,
mobile gaming systems, tablet computers, laptops, netbooks, among
other examples. Application management server 105 can provide one
or more services to the user devices to assist in the management of
applications downloaded, installed, used, or otherwise provided for
the user devices 110, 115, 120, 125. User devices 110, 115, 120,
125 can access application servers 140, such as centralized
application storefronts, such as, for example, Android Market.TM.,
iTunes.TM., and other examples. Application servers 140 can further
include, in some examples, other sources of software applications
that can be downloaded and installed on user devices 110, 115, 120,
125. User devices 110, 115, 120, 125 can communicate with and
consume the data and services of the application management server
105 over one or more networks 130, including local area networks
and wide area networks such as the Internet. Among the services of
an example application management server 105, applications
available to user devices 110, 115, 120, 125 can be analyzed,
assessed, and repaired at least in part by functionality provided
through application management server 105. Further, application
management server 105, in connection with services made available
to user devices 110, 115, 120, 125 can interact with and consume
resources, data, and services of other outside systems and servers
such as information servers 145. For instance, such information
servers 145 can host services and data that provide additional
intelligence and context regarding applications available to user
devices 110, 115, 120, 125, among other examples.
[0026] In general, "servers," "clients," "client devices," "user
devices," "mobile devices," "computing devices," "network
elements," "hosts," "system-type system entities," and "systems,"
including system devices in example computing environment 100
(e.g., 105, 110, 115, 120, 125, 140, 145, etc.), can include
electronic computing devices operable to receive, transmit,
process, store, or manage data and information associated with the
computing environment 100. As used in this document, the term
"computer," "processor," "processor device," or "processing device"
is intended to encompass any suitable processing device. For
example, elements shown as single devices within the computing
environment 100 may be implemented using a plurality of computing
devices and processors, such as server pools including multiple
server computers. Further, any, all, or some of the computing
devices may be adapted to execute any operating system, including
Linux.TM., UNIX.TM., Microsoft Windows.TM., Apple OS.TM., Apple
iOS.TM., Google Android.TM., Windows Server.TM., etc., as well as
virtual machines adapted to virtualize execution of a particular
operating system, including customized and proprietary operating
systems.
[0027] Further, servers, user devices, network elements, systems,
and other computing devices can each include one or more
processors, computer-readable memory, and one or more interfaces,
among other features and hardware. Servers can include any suitable
software component or module, or computing device(s) capable of
hosting and/or serving software applications and services (e.g.,
personal safety systems, services and applications of server 105,
etc.), including distributed, enterprise, or cloud-based software
applications, data, and services. For instance, in some
implementations, an application management server 105, application
servers 140, information servers 145, or other subsystems of
computing system 100 can be comprised at least in part by
cloud-implemented systems configured to remotely host, serve, or
otherwise manage data, software services and applications
interfacing, coordinating with, dependent on, or otherwise used by
other services and devices in system 100. In some instances, a
server, system, subsystem, or computing device can be implemented
as some combination of devices that can be hosted on a common
computing system, server, server pool, or cloud computing
environment and share computing resources, including shared memory,
processors, and interfaces.
[0028] User, endpoint, or client computing devices (e.g., 110, 115,
120, 125, etc.) can include traditional and mobile computing
devices, including personal computers, laptop computers, tablet
computers, smartphones, personal digital assistants, feature
phones, handheld video game consoles, desktop computers,
internet-enabled televisions, and other devices designed to
interface with human users and capable of communicating with other
devices over one or more networks (e.g., 130). Computer-assisted,
or "smart," appliances can include household and industrial devices
and machines that include computer processors and are controlled,
monitored, assisted, supplemented, or otherwise enhance the
functionality of the devices by the computer processor, other
hardware, and/or one or more software programs executed by the
computer processor. Computer-assisted appliances can include a
wide-variety of computer-assisted machines and products including
refrigerators, washing machines, automobiles, HVAC systems,
industrial machinery, ovens, security systems, and so on.
[0029] Attributes of user computing devices, computer-assisted
appliances, servers, and computing devices generally, can vary
widely from device to device, including the respective operating
systems and collections of software programs loaded, installed,
executed, operated, or otherwise accessible to each device. For
instance, computing devices can run, execute, have installed, or
otherwise include various sets of programs, including various
combinations of operating systems, applications, plug-ins, applets,
virtual machines, machine images, drivers, executable files, and
other software-based programs capable of being run, executed, or
otherwise used by the respective devices.
[0030] Some system devices can further include at least one
graphical display device and user interfaces, supported by computer
processors of the system devices, that allow a user to view and
interact with graphical user interfaces of applications and other
programs provided in system, including user interfaces and
graphical representations of programs interacting with applications
hosted within the system devices as well as graphical user
interfaces associated with application management server services
and other applications, etc. Moreover, while system devices may be
described in terms of being used by one user, this disclosure
contemplates that many users may use one computer or that one user
may use multiple computers.
[0031] While FIG. 1 is described as containing or being associated
with a plurality of elements, not all elements illustrated within
computing environment 100 of FIG. 1 may be utilized in each
alternative implementation of the present disclosure. Additionally,
one or more of the elements described in connection with the
examples of FIG. 1 may be located external to computing environment
100, while in other instances, certain elements may be included
within or as a portion of one or more of the other described
elements, as well as other elements not described in the
illustrated implementation. Further, certain elements illustrated
in FIG. 1 may be combined with other components, as well as used
for alternative or additional purposes in addition to those
purposes described herein.
[0032] Turning now to the example block diagram of FIG. 2, an
example system is shown including an application manager 205, user
system 210, among other computing devices and network elements
including, for instance, application servers 140 and information
servers 145 communicating over one or more networks 130. In one
example implementation, application manager 205 may include one or
more processor devices 215, memory elements 218, and one or more
other software and/or hardware-implemented components. For
instance, in one example implementation, an application manager 205
may include a share engine 220, user manager 222, healing engine
225, behavior analysis engine 228, application intelligence engine
230, among other potential machine executable logic, components and
functionality including combinations of the foregoing.
[0033] In one example, a share engine 220 can be configured to
provide functionality for managing crowdsourcing of information
relating to applications (e.g., made available by application
servers 140), as well as the sharing of such information and
resources, including resources generated at least in part by or
collected by application manager 205. For example, an example share
engine 220 can allow modified applications 232 developed for
particular users and associated user devices (e.g., 210) as well as
defined application modes 240 to be shared across multiple user
devices (e.g., 210), among other examples. An example user manager
222 can provide functionality for managing user accounts of various
user devices (e.g., 210) that consume or otherwise make use of
services of application manager 205. An example user manager 222
can associate various modified applications 232, application data
and feedback data (e.g., 235), and application modes 240, including
application modes developed or modified by particular users with
one or more user accounts and user devices (e.g., 210) in a system,
among other examples.
[0034] An application manager 205 can, in some implementations,
additionally include components, engines, and modules capable of
providing application management, security, and diagnostic services
to one or more user devices (e.g., 210) in connection with user
device attempts to download, install, activate, or otherwise use or
procure various applications including applications provided
through one or more application servers (e.g., 140). For instance,
in one example implementation, application manager 205 can include
an example behavior analysis engine 228 adapted to analyze and
identify functionality of various applications made available to
user devices on the system. Further, functionality of applications
can be identified, for instance, by behavior analysis engine 228,
that users or administrators may wish to block, limit, repair, or
modify, among other examples. Accordingly, in some implementations,
an example application manager 205 can include an example healing
engine 225 configured to modify applications on behalf of users to
eliminate undesirable application features detected, for example,
by behavior analysis engine 228 and thereby generate modified
applications 232. Modified applications 232 can, in some examples,
be specifically modified and configured based on the requests,
rules, settings, and preferences of a corresponding user.
Additionally, application manager 205 may include an application
intelligence engine 230 configured to collect application data
(e.g., 235), for instance, from information servers 145 and other
sources both internal and external application manager 205 and its
client user devices (e.g., 210). An application intelligence engine
230 can be used to collect intelligence regarding one or more
applications served, for instance, by application servers 144. The
intelligence can be used in connection with services provided by
application manager 205, such as behavior analysis and assessments
of applications by application manager 205, among other
examples.
[0035] In some implementations, a user device (e.g., 210) may
include one or more processor devices 242 and one or more memory
elements 245 as well as one or more other software- and/or
hardware-implemented components including, for example, a mode
manager 248, settings manager 252, security tools 250, and one or
more applications 255 (e.g., procured through application servers
140). In one example implementation, a user device 210 can include
a mode manager 248 that is equipped with functionality for
defining, enforcing, and otherwise managing multiple application
access modes 265 on the user device 210. Mode rules 270 can
additionally be managed by mode manager 248, the mode rules 270
defining, for instance, particular conditions for automatically
initiating or enforcing various modes 265 on the user device 210.
Additionally one or more settings 260 can be defined by users, for
instance, through an example settings manager 252, the setting
corresponding to and in some cases used in connection with various
modes 265 of the device 210, among other examples.
[0036] Turning to the example of FIG. 3, a simplified block diagram
300 is shown illustrating functionality and flows of an example
application manager. For example, a behavior monitor 228 can assess
applications to identify whether one or more functions and/or
content of an application are good, bad, suspect, or of unknown
quality, among other examples. The assessment can be based on
information acquired from a variety of sources (e.g., 145), such as
information servers, user feedback, and other sources. In instances
where "bad" application functionality and/or content is identified
an application healing engine 225 can be engaged to modify the
application and remediate the identified undesirable functionality
to generate a modified application file 232 corresponding to a
healed version of the application. Further, suspect or unknown
applications can be designated, for instance, by a mode manager
248, to be dedicated to a particular limited access mode of the
user device 210 so as to, in effect, quarantine the suspect
application until more intelligence is acquired regarding the
application's functionality. In instances where it is determined
that an application satisfies rules, requirements, or preferences
of a user, network, administrator, etc., the application may
instead be allowed to proceed for installation on a user device.
Further, applications which have been healed to generate a modified
application file can allow for the modified application to proceed
to the user device for installation on the device, among other
examples.
[0037] FIG. 4 includes a block diagram 400 illustrating example
principles and activities enabled through an example application
behavior analysis engine. Application binaries 405 can be accessed
or received by a disassembler data/control flow analyzer 410 which,
in combination with ambient application knowledge 415 (e.g.,
collected from outside information sources as well as users,
reviewers, etc.) such as application descriptions, reviews,
comments, and other structured and unstructured data, can develop a
model of the application logic 420 for each application binary 405.
The disassembler and control flow analyzer 410 can identify
behaviors 425 of the given application based on, for example,
comparing code or application logic model with known functionality
defined in or identifiable from a software development kit and/or
common APIs utilized by the corresponding client device operating
system as well as most or all applications compatible with the
client device. Some examples include the Google Android software
development kit, Apple iOS software development kit, Windows
software development kit, among other examples.
[0038] Generally, a platform software development kit (or "SDK")
can provide documentation, header files, libraries, commands,
interfaces, etc. defining and providing access to the various
platform subsystems accessible by applications compatible with the
platform. In one example implementation, a platform SDK and
corresponding APIs and API calls (i.e., calls to functions and
routines of the API) can be represented in a model that can be
used, for instance, by an application behavior engine, to determine
behavior and functionality of applications compatible with the
platform. The semantics of commonly used APIs is represented in a
program readable form along with critical information necessary to
derive application behavior. The semantics of the platform SDK can
be represented so that an example application behavior engine can
use the semantic model to understand and identify the operations
and behaviors of a given application using the API call. For
example, in one example implementation, all of the potential API
calls of the platform can be represented, for instance through API
intelligence 430, by tagging the name of each respective API call
with the behavioral tag describing what the respective API call
does on the platform as well as the corresponding parameters of the
API's operations and behaviors. As an example, a template of such a
semantic representation can be modeled, for instance, as:
TABLE-US-00001 <APIName: name <Category:
read/write/process/transform/.../...> <CategoryDetail>
<Reads: sensitivity> <Writes: sensitivity>
<Transform: sensitivity> <Senitivity:
red:5/orange:4/yellow:3/green:1> <Parameters: No of
paramerer> <ParameterIndex:Index> <Type:
integer/object/string/../..> <Operation:
input/output/transformative> <return value:
void/integer/object/string/> <Dependency>
<True/False> <Description> <APIDescription:
description of the API> <Verbs:xxx> <Nouns:xxx>
[0039] In the foregoing example, a "category" can designate the
type of an API call and be used to identify the general
functionality of such API calls, such as, that the API call reads
information from a particular subsystem, disk, etc. generates
various messages, initiates various network behaviors, attempts to
communicate with various outside servers, triggers particular
device functions or elements (e.g., a camera, SMS controller,
etc.). "Sensitivity" can represent the respective sensitivity of
the subsystem affected or associated by the API in the context of
the potential for malicious behavior in connection with the
subsystem, such as whether reading to a particular memory location
introduces the potential for spying, where the subsystem
potentially permits the introduction of malware, unauthorized
tracking or data collection, the unauthorized or undesired reading
or sending of SMS or email messages, among many other examples.
Further, "dependency" can represent whether the output of this API
can have an impact on other parts of the program in a direct way.
For instance, a sendTextMessage( ) API can be identified as having
no dependency where the API simply sends an SMS message out and
does not return anything, among other examples.
[0040] Other information can be used by a behavior heuristics/rule
engine 435 (e.g., of an example analysis engine (e.g., 228)) to
determine behaviors of an application under assessment, such as
global threat intelligence (GTI) 440 aggregating intelligence from
a community of sources 445, rules 450, and other information.
[0041] As noted above, an example application behavior analysis
engine (e.g., 228) can possess functionality for identifying the
control flows, operations, functionality, and behavior of a given
application based, for instance, on a semantic representation of a
standard platform SDK upon which compatible applications are based.
In FIG. SA, representation 500 of a simplified application control
now is shown for an example gaming application. While the
functionality of the game may be in the main desirable, secure, and
benign, deeper inspection of the code of the game application
binary in comparison with the semantic representation of the
platform SDK as well as ambient application intelligence for the
game application, may yield identification of other functionality
that is not immediately or otherwise identifiable, understood, or
appreciated by users, such as the application sending SMS messages
either with or without a user's explicit knowledge or permission.
In another example, shown in FIG. 5B, inspection of a particular
object of an application binary may reveal the totality of
functions and control flows of the given application as well as
reveal dependencies between distinct programs, program units, or
applications the user may not otherwise realize, understand, or
approve of. As an example, identified behavior heuristics can be
represented externally, in some implementations, in an XML file
that identifies the specific pattern of data flow and calls, from
which the behavior can be identified. For instance:
TABLE-US-00002 <Pattern> < Call to API1( ): mandatory <
Call to API2( )/API3( )/....: mandatory> < Call to API5(
)/API6( )/....: optional> < Call to API10( ): mandatory>
</Pattern>
[0042] In some implementations, based for instance on a model of
the semantic representation of the platform SDK, application logic
can be modeled and rules can be applied to interpret the
application logic and identify instructions and calls within a
corresponding binary of the application that correspond with
malicious, privacy infringing, policy violating, or other
undesirable behaviors. The logical model of an application's
functionality can include representation (e.g., 505) of the
application logic through data flow structures and control flow
structures, among other examples. A dataflow structure can
represent the lifetime of data objects as they pass-through the
application logic (e.g., 510) and onto other program units (e.g.,
515) including external program units. A dataflow structure (e.g.,
505) can be used to identify the flow of data from one part of the
application program as it moves and is potentially transformed by
the application logic. For example, a dataflow model can be used to
deduce that particular data is being leaked by the application
through an Internet communication post operation, among other
examples. Further, control flow structures can represent the
control flow of different function calls (e.g., 520, 525) to
identify an originating source of an application call determined to
be sensitive or undesirable. As an illustrative example, a call by
the application to send an SMS message can be traced back, for
example, to a UI element of an application interacted with by user,
or even an autonomous event in a background process of the
application, among potentially many other examples.
[0043] Turning to the examples of FIG. 6, a simplified block
diagram is illustrated representing various subsystems, devices,
and functionality accessible by applications through one or more
APIs defined in a platform SDK, for example. In some
implementations, all platform subsystems can be categorized or
assigned weights based on the sensitivity of the respective
subsystem in the context of the potential that the subsystem could
be manipulated or utilized in connection with a malicious or
otherwise undesirable behavior. Such weights and sensitivities can
be based on a variety of factors including, for example, the
potential for an invasion of privacy, data leaks, financial
sensitivity, among other examples. These factors can also form the
basis of categorizations of the various subsystems of the platform.
Such subsystems can include, for example, contact lists, photo
galleries, email clients, calendars, Internet connectivity and
browsing, graphics, video functionality, cameras, audio, security
tools and engines, telephony, Wi-Fi capabilities, Bluetooth
capabilities, data ports, battery power, touchscreens, global
positioning systems, among potentially many other functionalities
and subsystems including future functionality that can be
integrated in mobile devices.
[0044] As represented in the example of FIG. 7, a rule engine of an
application behavior analysis engine can access rules, for
instance, from a rule database, including rules that have been
custom defined for and/or by a particular user or set of users
according, for example, to preferences of the users as well as
policies applicable to the users (e.g., policies of an Internet
service provider, enterprise network, broadband data provider,
etc.). The rule engine can take as a further input an application
logic model (e.g., developed based on a semantic representation of
a platform SDK corresponding to the application) to assess the
various operations and functionality of an application as
identified in application logic model. The rule engine can assess
the various operations and functionality of an application
according to rules identified as applicable to the particular
instance of an application, such as an instance of an application
that has been attempted to be downloaded or installed on a
particular user computing device of a user associated with the
identified rules. Application behaviors can be identified by the
rule engine including application behaviors identified as violating
one or more rules (e.g., rules forbidding certain behaviors or
actions) and prompting, in some instances, remediation of the
identified application behaviors and/or assignment of the
application to one or more operation modes on the destination user
device, such as a quarantine or administrative operation mode,
among other examples.
[0045] In some implementations, a human readable description of a
behavior identified and based on a description of API semantics can
be constructed. In one example, human relatable verbs and nouns can
be associated with template messages in the semantic representation
and mapped to particular human understandable descriptions of
functions and operations available to the APIs. Further, in
connection with assessments of an application according to the
semantic model performed, for example, by an application behavioral
analysis engine, a human-readable summary of the behavior analysis
results can be generated from the mapping and presented to a user
that describes the various functionality, as well as, in some
implementations, the control flow dataflow of the analyzed
application. Such results can make use of the human readable
description to generate a description of the functionality
uncovered during analysis of the application, including
functionality that may otherwise be invisible to or difficult to
detect by the user. For example, in one implementation, the
template can be utilized and populated so as to identify and
describe an example application's functionality for reading SMS
data from the user's device. As an illustrative example,
corresponding description could be generated such as: "This
application reads your SMS data from SMS inbox and sends to a web
site." Such a description could be constructed, for example, by
filling in an example template based on the semantic representation
of the platform SDK and APIs such as: "This application <verb:
reads> your <noun:SMS data> from <noun: SMS inbox>
and <verb: sends> to a <noun: website>", among other
examples.
[0046] In some examples, the analyzed application behavior can
reveal the use of other applications, programs, or services by the
analyzed application. Some instances, a call to a local
application, remote service, or other program by the analyzed
application may be undesirable, for instance, when the other called
application is identified as unsecure, un-trusted, or unknown,
among other examples. In other instances, a program called or used
by the analyzed application may be identified as a trusted program.
Accordingly, in some implementations, an application behavior
analysis engine can make use of, generate, modify, and otherwise
manage whitelists and/or blacklists that identify the status and
reputations of various programs that have been known to or could be
potentially called by various analyzed applications. In some
implementations, applications and services hosted by remote servers
can additionally be identified in such whitelists and/or blacklists
by the respective URLs or other address information corresponding
to their respective host servers, among other examples.
[0047] In some implementations, the behavioral analysis engine can
identify the context in which a particular activity is performed,
platform API is accessed, or functionality is employed by the
application under assessment. As an example, an analyzed
application's attempts to access a platform telephony subsystem can
be assessed based upon the cause or context of the attempt. For
instance, in some contexts, a particular API call may be perfectly
acceptable while in other contexts the API call can be undesirable.
For instance identified application functionality that accesses the
telephony subsystem in response to a user interface interaction,
such as a button press, may be assessed differently than an attempt
by an application to access the telephony subsystem autonomously
and not in response to a user provided directive, among other
examples.
[0048] As noted above, in some implementations, rules can be
defined that can be used in the assessment of application
behaviors. Such rules can be represented and configured for use in
performing heuristic analysis of an application's logic or of a
potentially malicious behavior identified by an application
behavior analysis engine, including contexts in which the behavior
is to be determined to be malicious. For instance, a rule engine
can apply one or more rules to an application logic model to
identify one a more potentially malicious or otherwise undesirable
behaviors present in the application. In some implementations, a
rule can be represented as:
TABLE-US-00003 <Rule>
<Run><Dataflow><ReadOperation>of <red sub
system>to a<WriteOperation> of <write sub
system>
The rules can be generic or can be specific to a particular
subsystem, etc., such as a rule to detect data leak of a memory
element storing personal contact data, among other examples. A
specific application behavior can be derived based on application
of a single rule or multiple rules.
[0049] In some implementations, an application behavior analysis
engine can be hosted on one or more server computing devices remote
from the mobile user devices for which analysis performed. In other
examples, at least a portion of application behavior analysis
engine can be provided alternatively or redundantly with
functionality of server-side application behavior analysis engine
components. For instance, in one example implementation, a user
computing device can be provided with application behavior analysis
engine functionality allowing at least a partial or quick
preliminary assessment of an application to be performed at the
user device to thereby provide a user with fast feedback as well as
assess whether an application should be quarantined, denied
download or installation, and/or forwarded to a remote application
behavior analysis engine, such as one provided in a cloud system,
allowing then for a more robust behavioral analysis of the
application (that could possibly introduce increased latency into
the behavioral analysis assessment).
[0050] In some implementations, during an analysis of an
application, downloading, insulation, or launching of the analyzed
application may be prevented or delayed until the analysis is
completed. In some instances, a user can be provided with a prompt
identifying the analysis of the application as well as providing
the user with various options for dealing with the installation,
downloading, or launching of the analyzed application. For
instance, a user may be provided with the option of skipping the
analysis, delaying installation of the analyzed application,
assigning the analyzed application to a particular mode, among
other examples. Additionally, in some implementations, a prompt
presented to the user in connection with the assessment may be
presented together with information, such as preliminary
information, gleaned from the behavioral analysis engine
assessments and/or external intelligence relating to the analyzed
application. Such intelligence can include, for example,
intelligence gleaned by the behavioral analysis engine in previous
assessments of the analyzed application, among other examples.
Indeed, in some implementations, the behavioral analysis engine can
indicate to the user behaviors discovered for the application, how
other users have responded to feedback received from the behavioral
analysis engine regarding the particular analyzed application,
among other examples.
[0051] In some implementations, behavioral analysis engine can
maintain blacklists, greylists, and/or whitelists of applications
known to and/or previously analyzed by the behavioral analysis
engine. Such blacklists, greylists, and/or whitelists can be based
on historical intelligence collected from previous behavioral
analyses, outside intelligence from other sources, and other users.
The behavioral analysis engine can utilize such information to
perform an initial assessment of an application and leverage
information gleaned from previous analyses. Initial filtering or
feedback can thereby be provided to a user to assist the user in
determining how to deal with a particular application as well as
whether to initiate further behavioral analysis on the application
using the behavioral analysis engine.
[0052] Behavioral analysis of applications and/or
blacklists/whitelists can further incorporate or consider general
reputation information of developers or other parties identified as
responsible for various applications, among other examples and
considerations. Rules can be defined that consider the
trustworthiness or untrustworthiness of the developer, distributor,
etc. of an application. For example, an application development
score rating can be computed for a developer based on aggregate
analyses of applications of the developer by the behavioral
analysis engine. For instance, such a rating can be derived as:
AppDeveloper Rating=f(total number of apps, weighted average of
undesired behavior in apps, popularity of the app, average ratio of
low ratings), among other examples. For instance, in one
Illustrative example, a weighted average of undesired behavior can
be generated for a set of applications of a developer:
TABLE-US-00004 Weight No of Total Behavior (out of 10) occurrence
weight Contacts leakage 9 2 18 Device ID leakage 2 5 10 Message
Leakage (SMS) 8 3 24 Location leakage 5 4 20 Unnecessary
permissions 2 1 2
and average weight can be derived by Average Weight=Total
Weight/Total number of Apps, among other example
implementations.
[0053] Outside sources, such as intelligence databases, such as a
global threat intelligence (GTI) feed, can be used for identifying
malicious behaviors that have been detected across one or more
networks that may be employed by applications assessed by
behavioral analysis engines. For instance, various URLs, IP
addresses, phone numbers, and files can be identified that have
been previously determined to be associated with or used in other
malicious attacks, malware, or suspect systems. Additionally, a
behavioral analysis engine can interface with intelligence
databases to provide additional intelligence gleaned from the
behavioral analyses of applications performed by the behavioral
analysis engine itself, among other examples.
[0054] Further, in some systems and platforms, applications offered
by one or more application servers or storefronts may provide users
with basic descriptions, ratings, user feedback, etc. collected for
a given application. Unfortunately, in many instances, such
ratings, application descriptions, content ratings, etc. may be
provided by, manipulated by, or otherwise influenced by the
application developers themselves thereby diminishing, potentially,
the truthfulness or legitimacy of the information provided to users
regarding some applications. Accordingly, in some of
implementations, intelligence (e.g., behavioral descriptions)
gleaned from behavioral analyses of applications performed by an
example behavioral analysis engine may be used to supplement,
correct, or otherwise modify descriptions provided to users in
connection with their browsing, purchasing, and downloading of
applications available on a platform. Further, in some
implementations, a behavioral analysis engine can make use of these
default application descriptions, content ratings, user feedback
etc. as external intelligence considered in connection with a
behavioral analysis. In still other examples, a behavioral analysis
engine may be used to identify common behavioral traits between
multiple applications that can serve as the basis for categorizing
the applications according to behavior. Such categories can then be
provided to users to assist users in better understanding the
qualities and behaviors, as well as potential risks, of various
applications, among other examples.
[0055] Turning to FIG. 8, a simplified schematic diagram 800 is
shown of an example flow for performing deep analysis of
application behavior (e.g., using a behavioral analysis engine) and
performing application healing in an attempt to remedy those
behaviors determined to be undesirable in an application while
still preserving other core functionality of the application, in
some examples. As shown, application binaries can be submitted to a
disassembler and data control flow analyzer 410 (e.g., of a
behavior analysis engine) to develop application logic models
(e.g., 420) based, in some examples, additionally on ambient
application knowledge 415, intelligence, and the like. As noted
above, the model of application logic 420 can be assessed based on
defined rules, platform API intelligence, and behavioral heuristics
through a behavioral heuristics/rules engine 435 to identify
application behaviors of a respective application. Further,
sections of code of the application can be identified during the
assessment as responsible for the exhibited undesirable behavior.
This code can be flagged for remediation. Additionally, in
instances where application behaviors are identified as undesirable
and are requested or dictated, by a user, administrator, or
predefined rules, to be healed, the application binaries can be
further processed to remove, block, or otherwise remediate the
offending behaviors and corresponding code to thereby generate
healed versions 232 of the application binaries that a user can
then cause to be downloaded, installed, and executed on the user's
device. Additionally, as noted above, the global threat
intelligence feed 440 or other intelligence database can provide
intelligence for consideration and behavioral analyses as well as
application healing. Additionally, intelligence gleaned from the
behavioral analyses can be shared with outside intelligence
databases that additionally receive input, data, and intelligence
from a community of users and systems 445.
[0056] Turning now to the example of FIG. 9, an additional
flowchart 900 shown representing decisions made in connection with
the management and remediation of applications determined to
include undesirable behaviors based on behavioral analyses of the
applications. For instance, rules and policies can be defined, for
instance, by a user or system or network administrator, to define
how and under what conditions applications are to be handled that
have been determined to include one or more undesirable behaviors.
Such policies can, for example, identify particular types of
undesirable behaviors and map such behaviors to predefined courses
of action, such as the healing or remediation of the applications,
blacklisting or whitelisting of the applications, quarantining of
the applications, among other examples. Additionally, user inputs
can drive management of an application's deployment on a user
computing device. Such inputs can be received in connection with
prompts presented to the user and can include, for example,
requests to remediate one or more identified undesirable behaviors,
instructions to assign the analyzed application to a particular
operation mode or quarantine area, among other examples.
[0057] As noted above, static healing and personalization of
application behavior can be performed by a healing engine allowing
the code of the application to be modified and generate a "safe"
version of the application that allows the user to retain safe or
legitimate functionality of the application while removing
undesirable behaviors. Such healing can in some cases be
personalized or customized to particularly-defined policies driving
the healing, thereby allowing a user, service provider, device
manufacturer, etc. to control and personalize the functionality of
applications to be installed on corresponding user devices. In FIG.
10, simplified b diagram 1000 is illustrated showing the flow of an
example healing of an original application 1005. Upon identifying
1010 undesirable behaviors and offending sections of the code of
the application binary, a healing engine can be provided for
identifying, removing, replacing, or blocking, the offending code
and corresponding behaviors in order to generate a modified
application binary 1015. As an example, a healing engine 228 may
include logic for modifying an application by removing or blocking
various types of undesired behaviors such as, in this example,
unauthorized reads or accesses of SMS functionality by removing the
offending instructions discovered in the original application
binary. In other instances, such as shown in this example, a
healing engine may modify the offending code, such as by rewriting
the code to redirect an API call to a trusted system, destination,
address, etc. A healing engine 228 can modify the original code
with minimal changes so as to avoid affecting the core desired
functionality of the application. Further, healing policies can
identify the patterns that are considered for identifying
application code for healing. This can be represented, for example,
in an XML file that identifies the heuristic pattern of code
corresponding to an offending behavior. Each type of defined or
identified pattern of code can be healed by a specific healing
method, such as according to corresponding policies. Such methods
can be identified and defined in such a way that the healing does
not impact the rest of the application's functionality.
[0058] A variety of healing methods can be employed by an
application healer engine. For instance, a particular offending
line of code functionality can be identified as a final or leaf
node in a control chain. In such instances, the offending code may
be determined to be able to be suppressed or removed without
affecting other dependencies in the application, among other
examples. In another example, if a removal of a particular API call
is determined to likely have no impact on surrounding code, the
removal healing method can be applied. The nature and character of
APIs can be learned, for example, from the semantic platform SDK
representation, among other examples. In other instances, the
offending behavior can be from one or more sections of code and may
result in multiple methods of healing applied to remediate the
behavior, such as by replacing the data in a register to alter the
behavior of the API or redirecting of the API call to a new version
of the API with same interface by replacing the offending API code
with the new API code, among other examples. In instances where a
new version of an API is introduced, the new API may, for example,
do nothing and set the register status so as not to impact other
parts of the program, process the inputs in a different way to
avoid the undesired behavior, or do pre-processing and/or
post-processing of the input/output parameter and call the original
API, among other example techniques that resolve the undesirable
behavior.
[0059] Turning to FIG. 11, a simplified block diagram is
illustrated showing the identification of code relating to
particular undesirable behaviors. For instance, sections 1a and 1b
of application code can be identified as corresponding to a first,
detected, undesirable behavior and sections 2a and 2b can be
identified as corresponding to a second undesirable behavior of the
application. Accordingly, healing the application can include
modifying or replacing the identified offending sections of code
with code that modifies or suppresses the undesirable behaviors.
Further, healing policies can be identified corresponding to the
identified code or API calls to identify healing techniques for
modifying the offended code and remediating the undesired
behaviors.
[0060] In FIGS. 12A-12E, additional examples are illustrated of the
detection of undesirable behaviors as well as the remediation of
the undesirable behaviors. For example, in FIG. 12A, an example
code fragment allowing an application to send latitude and
longitude information to an outside server is shown as having been
processed to populate an API template, for instance, utilizing a
behavior analysis engine. As shown in FIG. 12B, portions of the
application code can be identified that correspond to the behavior
of collecting geo-positional data and sending the geo-positional
data to the outside server. In accordance with one example, the
offending lines of code can be replaced, for example with code that
masks or redirects the sending of the geo-positional data to
prevent the application from tracking user location, among other
examples. In another example, illustrated in FIG. 12C, a control
flow can be identified within an application along with
corresponding application code. As shown in the examples of FIGS.
12D-12E, remediation of a particular undesirable behavior can
include deletion of an offending line of code, among other
examples.
[0061] FIG. 13 illustrates an example flow 1300 in connection with
remediation of one or more detected undesirable behaviors of an
application. For instance, the connection with the dynamic
personalization of an application's behavior for particular user,
the composite behaviors of the application and corresponding code
segments can be identified. A user interface can be presented in
connection with the healing or customization of the application
allowing the user to select particular identified behaviors for
remediation or modifications. In one example implementation, the
user interface can be provided in connection with an application
healing engine with the user inputs directing how (e.g., which
identified behaviors) the application healing engine is to modify
the application. In another example, application healing engine can
insert one or more user interface controls into the original binary
of the application allowing the user at launch of the modified
application to dynamically enable, disable, or otherwise remediate
or customize the behavior of the application. For instance, based
on the selections of the user, an original section of the code
corresponding to an accepted behavior can be utilized in lieu of a
healed version of the same code, among other examples. Effectively,
each of the segments of the code where behavior is demonstrated can
be selectively turned off or on based on the user preferences and
inputs. Further, the user interface can provide a user with the
option of saving the settings of an application so that the
selection of a particular subset of application behaviors persists
and is available the next time the application is launched on the
user's device.
[0062] In some implementations, functionality can be provided to
define, enable, and employ defined usage modes on the user devices.
Traditionally, user devices, such as smart phones and tablet
computers, among other examples, are designed to support a single
user and application profile. However, a single operation profile
and mode may not be appropriate for all of the actual users of the
device or the situations in which the device is used. For instance,
a user may desire to loan their device to a friend for some short
period of time, but would like to nonetheless retain control of the
access to some of the sensitive applications and data on the
device, email applications, contacts, calendars, messaging
functionality, etc. In other instances, the user may desire to
allow a child to temporarily use the device, for example, to play
game, but would prefer for other applications (e.g., web browsers)
and access to certain device settings and data to be blocked from
the child. Additionally, users may desire to control usage of some
subset of the applications on the device to specific times,
locations, and situations. For instance, games and social
networking applications may be desired to be disabled during school
hours, among other examples.
[0063] FIG. 14A illustrates a simplified block diagram 1400a of an
example implementation of a mode manager. For instance, various
modes may be defined based on intelligence gleaned from the user
device as well as outside services. A user may define one or more
modes through a user interface and a mode manager, for instance, on
the device may manage access to the various modes, for example,
using dedicated credentials assigned to each of the modes.
Additionally, as noted above, an application monitoring service or
application behavioral analysis engine may recommend particular
applications for a quarantine or high-security mode available on
the user device. Accordingly, a user may define such modes to
restrict access to potentially risky or currently analyzed
applications to administrative, adult, or other trusted users,
among other examples.
[0064] FIG. 14B illustrates another simplified block diagram 1400b
illustrating principles of an application mode manager. An
application mode manager 248, in some implementations, may include
various modules and functionality such as a mode setup manager
1405, lock service 1410, lock manager 1415, credential manager
1420, application access manager 1425, application protection
service 1430, password engine 1435, among other examples. For
instance, in the illustrated example, the user with administrative
privileges can set up passwords or PINs and assign these
credentials to modes defined by the user, for instance, using a
mode setup manager. An access manager can utilize a credential
manager to verify whether valid credentials have been received that
allow a current user of the device to access one of a set of modes
defined for the device. In the event that incorrect credentials are
entered, a lock manager can invoke a lock service to lock out the
current user from one or more applications by assigning the user to
a restricted mode or locking out the user altogether.
[0065] In some implementations, a device mode can be composed of an
exclusion list or inclusion list. Device modes can be defined as
respective sets of applications that are either allowed or somehow
protected in that mode, in the sense their usage is prohibited or
limited. In some instances, an exclusion list can be defined for a
mode that indicates a particular subset of the applications and/or
subsystems of a device that are accessible under the corresponding
mode (i.e., with the remaining applications protected or locked in
that mode). For instance, a mode can be defined such as according
to: <ModeName, inclusion/Exclusion, Access PIN, App1, App2, App3
. . . App N>. In some instances, each device mode can be
protected and associated with a particular password. The master
mode can be defined that allows access to the entirety of the
device's functionality and applications. Accordingly, a master
password can be provided that enables access to the master mode.
Within the master mode, the user may be provided with access to a
management console for managing the set of modes available or
defined at the device. Accordingly the user may edit or define
modes through the management console, as well as activate or delete
predefined modes. An example management console can allow a user to
select, from a listing of applications, those applications the user
wishes to designate as protected or accessible in any given mode.
In some cases, a single application can be allowed or protected
under multiple different modes.
[0066] In some implementations, mode passwords may be stored in
encrypted memory. For instance, the password of each mode can be
encrypted using a key generated by the same password. A stored,
encrypted password can then be validated by decrypting the password
with a key generated from the password entered by the user. The
decrypted data can then be compared with the user-entered password.
Based on the password provided by user, a corresponding mode can be
identified and authenticated to allow access to the mode by the
user. In some implementations, the user may manually lock the
device or the device may lock itself, for instance, after a
prolonged period of inactivity. When attempting to unlock the
device or wake up the device a user may be again presented with a
login prompt requesting a password of one of the modes available
and defined for the device.
[0067] In some implementations, modes can be hierarchical. For
instance, a user logged into a higher level mode (i.e., a mode
providing a relatively greater level of access), may be able to
freely move to another mode without providing credentials for that
lower-level mode. On the other hand, a user who has been
authenticated to a lower level mode may be forced to enter
additional credentials when attempting to access another mode at a
higher level in the hierarchy than the lower-level mode to which
the user was previously authenticated. For example, in one
instance, four device modes can be defined where:
[0068] Mode1 is admin level mode;
[0069] Mode 2 guest level mode;
[0070] Mode 3 is guest level mode; and
[0071] Mode 4 is low privilege mode
and the hierarchy is defined as: Mode1>(Mode2 and Mode
3)>Mode 4, where Mode2 is the same level as Mode3, among other
example implementations.
[0072] In some implementations, configuration of the device can be
altered, customized, or at least partially restricted when certain
modes are active. For example, a particular mode can activate or
deactivate GPS functionality, data access, telephony, as well as
certain applications. Further, in some examples, device modes can
be provided that secure data of particular applications when mode.
For instance, once a new mode has been created and assigned a
corresponding access level to set of applications, the data of
these applications may be protected by encryption through a
separate encryption key. This can be implemented for example by
using an encrypting file system for encrypting files and folders,
among other examples.
[0073] In some implementations, the executable code of applications
can be secured to protect against applications being used in modes
that disallow access and/or use to one or more of the behaviors or
features of the application. For instance, in one implementation,
the application executable can be stored in encrypted secondary
storage. An operating system loader of the user device can gain
conditional unencrypted access to the executable code, in some
examples, only if the application is found in an allowed
application list for the active device mode in which access to the
application is attempted, among other potential
implementations.
[0074] In some examples, defining multiple device modes for a user
device can further result in the provision of multiple unique home
screens to be presented in each of the corresponding modes. As a
result, in such implementations, the appearance of a given home
screen can indicate to a user the mode that is active on the device
as well as access privileges available in that mode. In some
instances, home screens can include icons of applications that are
available within that corresponding mode, hiding or obscuring the
icons of other applications that are protected within that mode,
among other examples.
[0075] Further, in some instances, device modes can be created
automatically, for instance, based on identified behaviors and
security profiles of applications that are detected or loaded on
the user device. For instance, a mode manager can make use of
behavioral analyses performed, for example, by an example
application behavioral analysis engine, to identify applications
that exhibit a common category of behaviors or category of security
profiles. For instance, applications identified as permitting
access to online resources may be grouped and assigned dynamically
to one or more modes that have been defined as allowing such
access. Other modes, such as modes dedicated for underage users,
may be denied access to applications that allow users to access the
Internet, among other examples. Other example categories may
include applications that enable telephony or mobile messaging
functionality, applications that make use of subsystems that
utilize sensitive data, collect potentially private information
(e.g., cameras, voice recorders, GPS systems, etc.), and other
examples. In some implementations, ambient intelligence relating to
an application, such as an age rating (e.g., 7+, 12+, 18+ years,
etc.), user reviews, or other information may be used to categorize
applications and group them in various modes. For example, a
description of an application may include an age or maturity rating
as well as reasons for the maturity rating. Accordingly, in one
example, one or more modes may be defined, for example, that block
access by child users to applications with higher maturity ratings,
among other examples.
[0076] Other global or distributed intelligence can also be used to
develop information for a given application, such as illustrated in
the simplified block diagram 1900 of FIG. 19. For instance,
application information can be constructed from security
information regarding behaviors of an application from global
threat intelligence 440, publisher/developer reputation information
1905, app store feedback and reviews 1910, behavior analysis
results 1915, among other examples. Such information (e.g., 440,
1905, 1910, etc.) can be used in combination with behavioral
assessments 1915 of the applications (e.g., whether an application
potentially leaks data, provides location information, enables SMS
messaging, etc.) to assign certain applications to particular
device modes, such as quarantine or administrative modes, among
other examples. A user may further designate custom categories or
behaviors or select pre-defined categories or behaviors as the
basis for assignments of applications to respective modes rather
than individually selecting the applications for inclusion in one
or more modes on al a carte basis, among other examples.
[0077] Turning to the example of FIG. 15A, an example algorithm is
represented for the storing of password information associated with
a particular mode. FIG. 15B represents an example algorithm for
validating a password and identifying a mode to activate that
corresponds to the entered password. It should be appreciated that
the algorithms of FIGS. 15A-15B are non-limiting examples presented
merely for purposes of illustration and that other alternative
algorithms and implementations can be utilized in other
instances.
[0078] Turning to the example of FIG. 16, in some implementations,
modes defined by a given user may be provided, for instance, to an
application management service, cloud service or other service
(e.g., 1600) that allows one or more modes, as well as rules
associated with the modes, to be aggregated and shared with other
users. Additionally, shared device modes maintained by a mode
sharing service 1600 can be browsed and selected for download and
utilization on user devices 110, 120, allowing a user to provision
their own device with modes created by other users and shared using
the mode sharing service. Further, the user can provision the
shared mode, in some examples, by downloading and installing a
definition of the shared mode from the mode sharing service and
assigning a unique password to the newly installed mode. In still
other examples, mode configurations can be shared directly between
devices, with one device obtaining a new mode from another device
sharing the mode, for instance, through wireless peer-to-peer
technologies like Bluetooth, near field communications (NFC), WiFi,
and others.
[0079] In some implementations, such as shown in the example of
FIG. 17, modes can be activated automatically based on context
information detected, for example, by the device itself. A user, in
some examples, can configure (e.g., on the management console),
rules for automatically activating particular modes. For instance,
a particular mode can be activated automatically in response to the
detection of a specific context at the user device. Such contexts
can include, for example, detecting the location or proximity of
the device within a defined geo-fence, detecting that the device is
in proximity of other devices, detecting the device in range of
particular data networks, detecting a user of the device (e.g.,
based on user biometric information collected by the device), a
detected time of day, device battery status, usage activity (e.g.,
to guard against particular users spending too much time on the
device, etc.), whether the device is traveling or in motion (e.g.,
as detected through GPS functionality, accelerometers, or other
functionality on the device), among potentially many other
examples.
[0080] Turning now to the example of FIG. 18, in some
implementations, modes can be provisioned and configured through a
remote service, such as a cloud service, allowing a user to
activate/deactivate or define a mode remotely. Using such a
service, a user can create a mode remotely (e.g., using a computer
other than the target mobile user device) and provision one or more
modes to the target user device and also activate and deactivate
the mode on the user device from a remote location. Further, an
administrator can also use the service to provision such modes on
mobile user devices as well as define rules and contexts for
automatically activating, applying, or deactivating a given mode,
among other examples.
[0081] FIGS. 20A-20D illustrate example screenshots of user
interfaces showing particular features of some example
implementations of mode management on a mobile user device. For
instance, screenshot in FIG. 20A illustrates a user interface for
defining a new mode and mode password. A similar user interface can
be provided to allow a user to select and activate one of multiple
available modes on the device and/or provide credentials for the
selected mode. In some implementations, a user device may include
native login credentials or a native login manager. A mode manager
may be implemented as an application itself that overrides a native
login manager and replaces a native login screen with the
mode-specific login prompts (e.g., that allow the multi-mode
functionality of the user device). In some instances, a user may
not be able to visually distinguish that a user device is
provisioned with multiple modes, with the login screen capable of
accepting one of a plurality of different login codes, each login
code corresponding to a supported mode (including hidden modes)
provisioned on the user device.
[0082] The screenshot of FIG. 208 illustrates a view of a home
screen for a particular mode. As shown in this example, a set of
restricted applications can be designated that can only be accessed
by providing credentials to and activating a higher level mode
(e.g., that permits access of the restricted applications).
Further, a My Apps folder can provide access to those applications
that have been enabled in a current active mode. Screenshot of FIG.
20C provides another view of an example administrative screen that
permits users to activate, edit, or create new modes. Additionally,
example screenshot of FIG. 20D illustrates a user interface that
can be provided in some implementations of a mode manager allowing
a user to designate from a list of applications on the device which
applications are to be included or protected in a given mode, and
so on. It should be appreciated that the foregoing examples are
provided merely for the sake of illustrating certain principles and
should not be interpreted as limiting examples. Indeed, a variety
of different implementations, user interfaces, program
architectures, operating systems, SDK platforms, and method
sequences can be substituted for those examples described above
without diverting from the general principles illustrated and
described in this Specification.
[0083] FIGS. 21A-21C are flowcharts 2100a-c illustrating example
techniques in the management of applications on mobile user
computing devices. For instance, in the example of FIG. 21A, code
of a particular application can be analyzed 2105, for instance,
against a semantic representation of a platform, such as a
representation of a platform SDK and/or APIs. A set of behaviors of
the particular application can be identified 2110. At least one
undesirable behavior in the set of behaviors can be identified
2115, for instance, based on the user selection of one of the
identified set of behaviors or automatically according to rules
and/or policies defined (e.g., by a user or administrator) for
applications to be downloaded, installed, launched, or otherwise
used at a particular mobile computing device.
[0084] In the example of FIG. 218, a behavior can be identified
2120 and a set of behaviors detected for a particular application
(e.g., according to the principles of the example of FIG. 21A). A
section of code of the particular application can then be
identified 2125 corresponding to the identified behavior. A
remediation action can be performed 2130 on the identified section
of code to automatically remediate the behavior, for instance, in
response to an identification that the identified behavior is an
undesirable behavior, etc. The remediation action can result in the
dynamic generation of a "healed" version of the particular
application that retains at least a portion of its original
functionality, with the undesired functionality being blocked or
stripped from the healed version.
[0085] In the example of FIG. 21C, a particular one of a plurality
of modes can be activated 2140. The modes can be defined for a
particular user computing device and dictate what subset of the
functionality of the computing device and its software may be
accessible to a particular user having credentials for accessing a
respective mode in the plurality of modes. Access can be restricted
2145 to one or more applications installed on the user computing
device according to the activation 2140 of the particular mode. In
addition, in some implementations, activation of the particular
mode can result in a restricted or alternate configuration of the
computing device to be applied that thereby limits a user's access
to one or more subsystems and functionality, including hardware
functionality, and settings and data of the user computing device,
among other examples.
[0086] Although this disclosure has been described in terms of
certain implementations and generally associated methods,
alterations and permutations of these implementations and methods
will be apparent to those skilled in the art. For example, the
actions described herein can be performed in a different order than
as described and still achieve the desirable results. As one
example, the processes depicted in the accompanying figures do not
necessarily require the particular order shown, or sequential
order, to achieve the desired results. In certain implementations,
multitasking and parallel processing may be advantageous.
Additionally, diverse user interface layouts and functionality can
be supported. Other variations are within the scope of the
following claims.
[0087] Embodiments of the subject matter and the operations
described in this specification can be implemented in digital
electronic circuitry, or in computer software, firmware, or
hardware, including the structures disclosed in this specification
and their structural equivalents, or in combinations of one or more
of them. Embodiments of the subject matter described in this
specification can be implemented as one or more computer programs,
i.e., one or more modules of computer program instructions, encoded
on computer storage medium for execution by, or to control the
operation of, data processing apparatus. Alternatively or in
addition, the program instructions can be encoded on an
artificially generated propagated signal, e.g., a machine-generated
electrical, optical, or electromagnetic signal that is generated to
encode information for transmission to suitable receiver apparatus
for execution by a data processing apparatus. A computer storage
medium can be, or be included in, a computer-readable storage
device, a computer-readable storage substrate, a random or serial
access memory array or device, or a combination of one or more of
them. Moreover, while a computer storage medium is not a propagated
signal per se, a computer storage medium can be a source or
destination of computer program instructions encoded in an
artificially generated propagated signal. The computer storage
medium can also be, or be included in, one or more separate
physical components or media (e.g., multiple CDs, disks, or other
storage devices), including a distributed software environment or
cloud computing environment.
[0088] Networks, including core and access networks, including
wireless access networks, can include one or more network elements.
"Network elements" can encompass various types of routers,
switches, gateways, bridges, load balancers, firewalls, servers,
inline service nodes, proxies, processors, modules, or any other
suitable device, component, element, or object operable to exchange
information in a network environment. A network element may include
appropriate processors, memory elements, hardware and/or software
to support (or otherwise execute) the activities associated with
using a processor for screen management functionalities, as
outlined herein. Moreover, the network element may include any
suitable components, modules, interfaces, or objects that
facilitate the operations thereof. This may be inclusive of
appropriate algorithms and communication protocols that allow for
the effective exchange of data or information.
[0089] The operations described in this specification can be
implemented as operations performed by a data processing apparatus
on data stored on one or more computer-readable storage devices or
received from other sources. The terms "data processing apparatus,"
"processor," "processing device," and "computing device" can
encompass all kinds of apparatus, devices, and machines for
processing data, including by way of example a programmable
processor, a computer, a system on a chip, or multiple ones, or
combinations, of the foregoing. The apparatus can include general
or special purpose logic circuitry, e.g., a central processing unit
(CPU), a blade, an application specific integrated circuit (ASIC),
or a field-programmable gate array (FPGA), among other suitable
options. While some processors and computing devices have been
described and/or illustrated as a single processor, multiple
processors may be used according to the particular needs of the
associated server. References to a single processor are meant to
include multiple processors where applicable. Generally, the
processor executes instructions and manipulates data to perform
certain operations. An apparatus can also include, in addition to
hardware, code that creates an execution environment for the
computer program in question, e.g., code that constitutes processor
firmware, a protocol stack, a database management system, an
operating system, a cross-platform runtime environment, a virtual
machine, or a combination of one or more of them. The apparatus and
execution environment can realize various different computing model
infrastructures, such as web services, distributed computing and
grid computing infrastructures.
[0090] A computer program (also known as a program, software,
software application, script, module, (software) tools, (software)
engines, or code) can be written in any form of programming
language, including compiled or interpreted languages, declarative
or procedural languages, and it can be deployed in any form,
including as a standalone program or as a module, component,
subroutine, object, or other unit suitable for use in a computing
environment. For instance, a computer program may include
computer-readable instructions, firmware, wired or programmed
hardware, or any combination thereof on a tangible medium operable
when executed to perform at least the processes and operations
described herein. A computer program may, but need not, correspond
to a file in a file system. A program can be stored in a portion of
a file that holds other programs or data (e.g., one or more scripts
stored in a markup language document), in a single file dedicated
to the program in question, or in multiple coordinated files (e.g.,
files that store one or more modules, sub programs, or portions of
code). A computer program can be deployed to be executed on one
computer or on multiple computers that are located at one site or
distributed across multiple sites and interconnected by a
communication network.
[0091] Programs can be implemented as individual modules that
implement the various features and functionality through various
objects, methods, or other processes, or may instead include a
number of sub-modules, third party services, components, libraries,
and such, as appropriate. Conversely, the features and
functionality of various components can be combined into single
components as appropriate. In certain cases, programs and software
systems may be implemented as a composite hosted application. For
example, portions of the composite application may be implemented
as Enterprise Java Beans (EJBs) or design-time components may have
the ability to generate run-time implementations into different
platforms, such as J2EE (Java 2 Platform, Enterprise Edition), ABAP
(Advanced Business Application Programming) objects, or Microsoft's
.NET, among others. Additionally, applications may represent
web-based applications accessed and executed via a network (e.g.,
through the Internet). Further, one or more processes associated
with a particular hosted application or service may be stored,
referenced, or executed remotely. For example, a portion of a
particular hosted application or service may be a web service
associated with the application that is remotely called, while
another portion of the hosted application may be an interface
object or agent bundled for processing at a remote client.
Moreover, any or all of the hosted applications and software
service may be a child or sub-module of another software module or
enterprise application (not illustrated) without departing from the
scope of this disclosure. Still further, portions of a hosted
application can be executed by a user working directly at a server
hosting the application, as well as remotely at a client.
[0092] The processes and logic flows described in this
specification can be performed by one or more programmable
processors executing one or more computer programs to perform
actions by operating on input data and generating output. The
processes and logic flows can also be performed by, and apparatus
can also be implemented as, special purpose logic circuitry, e.g.,
an FPGA (field programmable gate array) or an ASIC (application
specific integrated circuit).
[0093] Processors suitable for the execution of a computer program
include, by way of example, both general and special purpose
microprocessors, and any one or more processors of any kind of
digital computer. Generally, a processor will receive instructions
and data from a read only memory or a random access memory or both.
The essential elements of a computer are a processor for performing
actions in accordance with instructions and one or more memory
devices for storing instructions and data. Generally, a computer
will also include, or be operatively coupled to receive data from
or transfer data to, or both, one or more mass storage devices for
storing data, e.g., magnetic, magneto optical disks, or optical
disks. However, a computer need not have such devices. Moreover, a
computer can be embedded in another device, e.g., a mobile
telephone, a personal digital assistant (PDA), tablet computer, a
mobile audio or video player, a game console, a Global Positioning
System (GPS) receiver, or a portable storage device (e.g., a
universal serial bus (USB) flash drive), to name just a few.
Devices suitable for storing computer program instructions and data
include all forms of non-volatile memory, media and memory devices,
including by way of example semiconductor memory devices, e.g.,
EPROM, EEPROM, and flash memory devices; magnetic disks, e.g.,
internal hard disks or removable disks; magneto optical disks; and
CD ROM and DVD-ROM disks. The processor and the memory can be
supplemented by, or incorporated in, special purpose logic
circuitry.
[0094] To provide for interaction with a user, embodiments of the
subject matter described in this specification can be implemented
on a computer having a display device, e.g., a CRT (cathode ray
tube) or LCD (liquid crystal display) monitor, for displaying
information to the user and a keyboard and a pointing device, e.g.,
a mouse or a trackball, by which the user can provide input to the
computer. Other kinds of devices can be used to provide for
interaction with a user as well; for example, feedback provided to
the user can be any form of sensory feedback, e.g., visual
feedback, auditory feedback, or tactile feedback; and input from
the user can be received in any form, including acoustic, speech,
or tactile input. In addition, a computer can interact with a user
by sending documents to and receiving documents from a device,
including remote devices, which are used by the user.
[0095] Embodiments of the subject matter described in this
specification can be implemented in a computing system that
includes a back end component, e.g., as a data server, or that
includes a middleware component, e.g., an application server, or
that includes a front end component, e.g., a client computer having
a graphical user interface or a Web browser through which a user
can interact with an implementation of the subject matter described
in this specification, or any combination of one or more such back
end, middleware, or front end components. The components of the
system can be interconnected by any form or medium of digital data
communication, e.g., a communication network. Examples of
communication networks include any internal or external network,
networks, sub-network, or combination thereof operable to
facilitate communications between various computing components in a
system. A network may communicate, for example, Internet Protocol
(IP) packets, Frame Relay frames, Asynchronous Transfer Mode (ATM)
cells, voice, video, data, and other suitable information between
network addresses. The network may also include one or more local
area networks (LANs), radio access networks (RANs), metropolitan
area networks (MANs), wide area networks (WANs), all or a portion
of the Internet, peer-to-peer networks (e.g., ad hoc peer-to-peer
networks), and/or any other communication system or systems at one
or more locations.
[0096] The computing system can include clients and servers. A
client and server are generally remote from each other and
typically interact through a communication network. The
relationship of client and server arises by virtue of computer
programs running on the respective computers and having a
client-server relationship to each other. In some embodiments, a
server transmits data (e.g., an HTML page) to a client device
(e.g., for purposes of displaying data to and receiving user input
from a user interacting with the client device). Data generated at
the client device (e.g., a result of the user interaction) can be
received from the client device at the server.
[0097] While this specification contains many specific
implementation details, these should not be construed as
limitations on the scope of any inventions or of what may be
claimed, but rather as descriptions of features specific to
particular embodiments of particular inventions. Certain features
that are described in this specification in the context of separate
embodiments can also be implemented in combination in a single
embodiment. Conversely, various features that are described in the
context of a single embodiment can also be implemented in multiple
embodiments separately or in any suitable subcombination. Moreover,
although features may be described above as acting in certain
combinations and even initially claimed as such, one or more
features from a claimed combination can in some cases be excised
from the combination, and the claimed combination may be directed
to a subcombination or variation of a subcombination.
[0098] The following examples pertain to embodiments in accordance
with this Specification. One or more embodiments may provide an
apparatus, a system, a machine readable medium, and a method to
analyze code of a particular application against a semantic model
of a software development kit of a particular platform, identify,
based on the analysis of the code, a set of behaviors of the
particular application, and identify that one or more of the set of
behaviors are undesired behaviors. The semantic model can associate
potential application behaviors with one or more of APIs of the
particular platform.
[0099] In one example, identifying that one or more of the set of
behaviors are undesired behaviors includes determining that the one
or more behaviors violate one or more rules. The rules can be
associated with a particular user.
[0100] In one example, a user input identifies one or more of the
set of behaviors as undesirable. The user input can be received in
connection with a user interface displaying human readable
descriptions of the identified set of behaviors.
[0101] In one example, code of the particular application can be
disassembled into a control flow and a model of application logic
for the particular application can be generated based at least in
part on the semantic model. The model of application logic can be
further based, at least in part, on ambient application
knowledge.
[0102] In one example, a remediation action can be performed based
on the identification that one or more of the set of behaviors are
undesired behaviors.
[0103] In one example, the code of the particular application is
analyzed in connection with an attempt to implement the particular
application on a particular user device.
[0104] One or more embodiments may provide an apparatus, a system,
a machine readable medium, and a method to identify a particular
behavior in a set of behaviors detected as included in a particular
application, identify a section of code of the particular
application corresponding to the particular behavior, and perform a
remediation action on the section of code to remediate the
particular behavior and generate a healed version of the particular
application.
[0105] In one example, the remediation action preserves other
behaviors of the particular application other than the particular
behavior.
[0106] In one example, the remediation action includes deleting the
section of code.
[0107] In one example, the remediation action includes rewriting
the section of code.
[0108] In one example, the remediation action includes adding
additional code to the application to nullify the particular
behavior.
[0109] In one example, the remediation action is identified from a
policy identifying a remediation pattern determined to be
applicable to remedying the particular behavior.
[0110] In one example, the remediation action includes inserting
application logic allowing a user to selectively enable a healed
version of the particular behavior at launch of the healed
application on a user device. The user can be further allowed to
selectively enable an unhealed version of the particular behavior
in lieu of the healed version.
[0111] In one example, the set of behaviors of the particular
application can be detected through an analysis of code of the
particular application.
[0112] In one example, the remediation action is triggered by a
user request.
[0113] One or more embodiments may provide an apparatus, a system,
a machine readable medium, and a method to activate a particular
one of a plurality of modes defined for a particular user device,
and restrict access to one or more applications installed on the
particular user device in accordance with the activated particular
mode. The restricted applications can be accessible when another
one of the plurality of modes is activated.
[0114] In one example, the particular mode is activated in response
to a particular passcode entered by a user of the particular user
device, where each of the plurality of modes is associated with a
corresponding passcode. Activation of the particular mode can
include identifying the particular mode from the plurality of modes
based on the entry of the particular passcode, and authenticating
access to the particular mode based on the entry of the particular
passcode.
[0115] In one example, one or more of the plurality of modes are
user-defined modes.
[0116] In one example, an alternate device configuration can be
applied to the particular user device based on activation of the
particular mode. The alternate device configuration can restrict
access to one or more subsystems of the particular user device.
[0117] In one example, one of the plurality of modes is an
administrative modes allowing for modification of the plurality of
modes.
[0118] In one example, at least one of the plurality of modes is an
instance of a mode downloadable from a mode sharing service remote
from the particular user device.
[0119] In one example, the particular mode is activated
automatically based at least in part on the detection of a
particular context using functionality of the particular user
device.
[0120] In one example, at least a particular one of the
applications is restricted based on a defined rule for the
particular mode.
[0121] In one example, the defined rule pertains to detected
behavior of the particular application.
[0122] In one example, the plurality of modes includes a mode
designated as a quarantine mode for application awaiting behavioral
analysis or remediation.
[0123] In one example, the particular mode is activated in response
to a user command received at a device remote from the particular
user device.
[0124] Similarly, while operations are depicted in the drawings in
a particular order, this should not be understood as requiring that
such operations be performed in the particular order shown or in
sequential order, or that all illustrated operations be performed,
to achieve desirable results. In certain circumstances,
multitasking and parallel processing may be advantageous. Moreover,
the separation of various system components in the embodiments
described above should not be understood as requiring such
separation in all embodiments, and it should be understood that the
described program components and systems can generally be
integrated together in a single software product or packaged into
multiple software products.
[0125] Thus, particular embodiments of the subject matter have been
described. Other embodiments are within the scope of the following
claims. In some cases, the actions recited in the claims can be
performed in a different order and still achieve desirable results.
In addition, the processes depicted in the accompanying figures do
not necessarily require the particular order shown, or sequential
order, to achieve desirable results.
* * * * *