U.S. patent application number 17/689522 was filed with the patent office on 2022-06-16 for real-time detection and blocking of counterfeit websites.
The applicant listed for this patent is Bolster, Inc.. Invention is credited to Abhishek Dubey, Shashi Prakash.
Application Number | 20220188402 17/689522 |
Document ID | / |
Family ID | |
Filed Date | 2022-06-16 |
United States Patent
Application |
20220188402 |
Kind Code |
A1 |
Prakash; Shashi ; et
al. |
June 16, 2022 |
Real-Time Detection and Blocking of Counterfeit Websites
Abstract
Counterfeit uniform resource locators (URLs) are detected and
blocked in real-time by a browser extension in communication with a
counterfeit URL detection system. The browser extension receives a
URL requested within a browser application. Content from a webpage
associated with the received URL is extracted and transmitted to
the counterfeit URL detection system, which is configured to
analyze the content and return an assessment indicating whether the
URL is counterfeit. If the assessment indicates that the URL is
counterfeit, the browser extension blocks the browser application
from accessing content associated with the URL.
Inventors: |
Prakash; Shashi; (Mountain
View, CA) ; Dubey; Abhishek; (Mountain View,
CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Bolster, Inc. |
Los Altos |
CA |
US |
|
|
Appl. No.: |
17/689522 |
Filed: |
March 8, 2022 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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16260994 |
Jan 29, 2019 |
11301560 |
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17689522 |
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62628894 |
Feb 9, 2018 |
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International
Class: |
G06F 21/51 20060101
G06F021/51; G06F 21/55 20060101 G06F021/55; H04L 9/40 20060101
H04L009/40 |
Claims
1. A method comprising: receiving at a browser extension operating
in a browser application, a uniform resource locator (URL)
requested within the browser application; extracting by the browser
extension, content from a webpage associated with the received URL;
transmitting the extracted content to a counterfeit URL detection
system configured to analyze the extracted content and return an
assessment indicating whether the received URL is counterfeit,
wherein the analysis of the extracted content includes one of image
object detection; natural language processing, analyzing a
hypertext transfer protocol (HTTP) request header or body, and
analyzing an HTTP response header or body; and responsive to the
assessment indicating that the received URL is counterfeit,
blocking, by the browser extension, the browser application from
accessing content associated with the received URL.
2. The method of claim 1, further comprising: accessing a data
store that stores for each of a plurality of known URLs, an
assessment of an authenticity of the known URL; responsive to the
data store identifying the received URL as counterfeit, blocking
the browser application from accessing the content associated with
the received URL; and responsive to the data store not containing
the received URL, extracting the content from the webpage.
3. The method of claim 1, further comprising recording by the
browser extension a user behavior associated with the received
URL.
4. The method of claim 3, wherein the user behavior comprises a
number of URLs requested in a specified period of time.
5. The method of claim 3, wherein the user behavior comprises a
number of counterfeit URLs blocked in a specified period of
time.
6. The method of claim 3, wherein the user behavior comprises a
source of the received URL.
7. The method of claim 1, wherein the counterfeit URL detection
system determines a score, and wherein the assessment of whether
the received URL is counterfeit is based at least in part on the
score.
8. The method of claim 1, wherein the assessment of whether the
received URL is counterfeit is based at least in part on a
user-input threat tolerance.
9. The method of claim 1, further comprising comparing the received
URL with a stored URL, determining that only a portion of the
received URL is identical to the stored URL, and using a heuristic
to determine, based on the identical portion, whether the received
URL is counterfeit.
10. A non-transitory computer-readable storage medium storing a
browser extension that comprises computer program instructions, the
computer program instructions when executed by a processor causing
the processor to: receive a uniform resource locator (URL); extract
content from a webpage associated with the received URL; transmit
the extracted content to a counterfeit URL detection system
configured to analyze the extracted content and return an
assessment indicating whether the received URL is counterfeit,
wherein the analysis of the extracted content includes one of image
object detection; natural language processing, analyzing a
hypertext transfer protocol (HTTP) request header or body, and
analyzing an HTTP response header or body; and responsive to the
assessment indicating that the received URL is counterfeit, block
access to content associated with the received URL.
11. The non-transitory computer-readable storage medium of claim
10, wherein the computer program instructions when executed by the
processor further cause the processor to: access a data store that
stores for each of a plurality of known URLs, an assessment of an
authenticity of the known URL; responsive to the data store
identifying the received URL as counterfeit, block access to the
content associated with the received URL; and responsive to the
data store not containing the received URL, extract the content
from the webpage.
12. The non-transitory computer-readable storage medium of claim
10, wherein the computer program instructions when executed by the
processor further cause the processor to record a user behavior
associated with the received URL.
13. The non-transitory computer-readable storage medium of claim
12, wherein the user behavior comprises a number of URLs requested
in a specified period of time.
14. The non-transitory computer-readable storage medium of claim
12, wherein the user behavior comprises a number of counterfeit
URLs blocked in a specified period of time.
15. The non-transitory computer-readable storage medium of claim
12, wherein the user behavior comprises a source of the received
URL.
16. The non-transitory computer-readable storage medium of claim
10, wherein the counterfeit URL detection system determines a
score, wherein the assessment of whether the received URL is
counterfeit is based in part on a threshold relative to the
score.
17. A method comprising: receiving from a browser extension
executed by a user device, content extracted from a webpage
associated with a uniform resource locator (URL) requested by a
user of the user device; applying to the received content a model
trained to output an assessment indicating whether the URL is
counterfeit, wherein the assessment is based at least in part on
one of image object detection, natural language processing,
analyzing a hypertext transfer protocol (HTTP) request header or
body, and analyzing an HTTP response header or body; and returning
the assessment to the browser extension, wherein the browser
extension is configured to block access to the webpage responsive
to the assessment indicating that the URL is counterfeit.
18. The method of claim 17, further comprising: receiving from the
browser extension, data describing user behavior associated with
one or more URLs; and generating analytics that quantify the user
behavior.
19. The method of claim 18, wherein the analytics comprise an
identification of users who accessed at least a threshold number of
counterfeit URLs in a specified period of time.
20. The method of claim 18, wherein the analytics comprise an
identification of a source that provided at least a threshold
number of counterfeit URLs.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This disclosure is a continuation of U.S. Nonprovisional
patent application Ser. No. 16/260,994, filed Jan. 29, 2019, titled
"Real-Time Detection and Blocking of Counterfeit Websites," listing
as first inventor Shashi Prakash, which in turn claims the benefit
of U.S. Provisional Patent Application No. 62/628,894, filed Feb.
9, 2018, titled "System to Detect and Block Counterfeit Websites in
Real-Time," listing as first inventor Shashi Prakash, each of which
is incorporated herein by reference in its entirety.
TECHNICAL FIELD
[0002] This disclosure relates to detecting and blocking access to
counterfeit websites in real time.
BACKGROUND
[0003] Counterfeit websites are used for a variety of nefarious
purposes. These websites are created with intent to make users
believe they are using a legitimate site of a known entity,
deceiving the users into providing sensitive personal or financial
information or downloading potentially dangerous files. In some
cases, links to counterfeit websites may be sent to the user in a
message, such as an email, SMS message, or instant message. In
other circumstances, a nefarious website may have an address
similar to that of a popular, trusted website, such that a user is
directed to the nefarious website if a user mistypes the address of
the popular website into a browser. Because the harm that these
counterfeit websites or their operators can cause to a user may be
severe, it is desirable to block access to these websites.
BRIEF DESCRIPTION OF THE DRAWINGS
[0004] FIG. 1 is a block diagram illustrating an environment in
which counterfeit website detection is performed, according to one
embodiment.
[0005] FIG. 2 is a block diagram illustrating functional modules
within a browser extension, according to one embodiment.
[0006] FIG. 3 is a block diagram illustrating functional modules
within a counterfeit URL detection system, according to one
embodiment.
[0007] FIG. 4 is a flowchart illustrating a process for blocking
user access to counterfeit websites in real-time, according to one
embodiment.
[0008] FIG. 5 is a flowchart illustrating a process for analyzing
whether URLs are counterfeit, according to one embodiment.
[0009] FIG. 6 is a block diagram illustrating an example of a
processing system.
DETAILED DESCRIPTION
System Overview
[0010] Counterfeit uniform resource locators (URLs) are detected
and blocked in real-time by a browser extension in communication
with a counterfeit URL detection system. The browser extension,
configured for example as an extension within a web browser, email
client, or mobile application, protects users against nefarious
websites by intercepting a request to access a counterfeit URL and
blocking the web browser, email client, or mobile application from
accessing the nefarious content. In some embodiments, the browser
extension receives a URL requested within a browser application.
Content from a webpage associated with the received URL is
extracted and transmitted to the counterfeit URL detection system,
which is configured to analyze the content and return an assessment
indicating whether the URL is counterfeit. If the assessment
indicates that the URL is counterfeit, the browser extension blocks
the browser application from accessing content associated with the
URL.
[0011] As used herein, a "counterfeit URL" refers to an address
that references an untrusted webpage. These webpages may exhibit
nefarious behaviors, such as phishing for sensitive information
from a user or causing malicious content to be downloaded to a
user's device, or may emulate other websites in order to deceive
users into believing that the webpage is affiliated with a trusted
source. Some counterfeit URLs may mimic the URL of a well-known
website so that the user believes she is accessing the well-known
website. For example, if a user is familiar with www.example.com,
the user may believe she is accessing the familiar webpage when in
reality she is requesting the counterfeit URL www.example.com.
Other counterfeit URLs may redirect the browser to nefarious
webpages, such that a user's careful inspection of the requested
URL may not reveal information about the webpage ultimately
displayed by the browser.
[0012] FIG. 1 is a block diagram illustrating an environment in
which counterfeit website detection is performed, according to one
embodiment. As shown in FIG. 1, the environment can include a user
device 110, one or more third-party servers 120, and a counterfeit
URL detection system 130 communicating over a network 140. The
network 140 enables communication between the user device 110,
third party servers 120, and counterfeit URL detection system 130,
and may include one or more local area networks (LANs), wide-area
networks (WANs), metropolitan area networks (MANs), and/or the
Internet.
[0013] The user device 110 is a computing device used by a user to
access content over the network 140 and can be any device capable
of displaying electronic content and communicating over the network
140, such as a desktop computer, laptop or notebook computer,
mobile phone, tablet, eReader, television set, or set top box. In
some cases, the user device 110 can be configured as part of an
enterprise, representing a plurality of user devices 110 associated
with an organization such as a company.
[0014] The user device 110 executes a browser application 112,
comprising software that when executed by the user device 110
retrieves and displays electronic documents. Other applications can
additionally be executed by the user device 110, such as an email
application, a short messaging service (SMS) application, or other
applications capable of receiving and sending electronic
messages.
[0015] As used herein, the browser application 112 can refer to any
application capable of retrieving electronic content over the
network 140, including web browsers, mobile applications, or email
applications. The browser application 112 includes a user interface
enabling users to interact with electronic content by, for example,
displaying the content to the user, providing a navigation or
address bar for users to input URLs to request desired content, and
rendering selectable hyperlinks embedded within content that can be
selected to cause the browser application 112 to retrieve
additional content. The browser application 112 may also include a
networking engine that retrieves content associated with a URL when
the URL is requested by explicit user action or by a call from an
external application. For example, a user may explicitly request
the browser application 112 access a URL by typing or pasting a
copied URL into an address bar in the browser user interface. As
another example, if a user selects a hyperlink in an email that
contains a URL, the email application may generate a call to the
browser application 112 to cause the browser 112 to access a
webpage identified by the URL.
[0016] A browser extension 116 operates within or parallel to the
browser application 112 and extends functionality of the browser
application 112. The browser extension 116, which for example can
comprise computer program instructions provided by the counterfeit
URL detection system 130 and executable by a processor of the user
device 110, can receive a URL requested by the browser application
112. Before the browser application 112 retrieves and displays
content associated with the webpage identified by the URL, the
browser extension 116 determines whether the URL is counterfeit. If
the URL is determined to be counterfeit, the extension 116 blocks
the browser application 112 from displaying the webpage content. If
the page is determined to not be counterfeit, the extension 116
allows the browser application 112 to display the content (for
example, by taking no action to block the content). The browser
extension 116 is described further with respect to FIG. 2.
[0017] The third-party servers 120 store electronic content and
serve the content to the user device 110 when requested. The
third-party servers 120 can be computing devices associated with
any of a variety of sources of content that may be requested by a
user, such as banks, online retailers, or government entities. Some
of the third-party servers 120 may be associated with a malicious
actor and serve counterfeit websites that are designed to look like
or deceive users into believing they are associated with a trusted
content source.
[0018] The counterfeit URL detection system 130 analyzes URLs and
webpage content to determine whether a webpage provided by a
third-party server 120 is authentic or counterfeit. In some cases,
the detection system 130 is configured as part of an enterprise
shared with a plurality of user devices 110, for example
communicating with the user devices 110 over a local area network
or behind a firewall shared with the user devices 110. In other
cases, the detection system 130 is remote and operated
independently from the user device 110, for example on one or more
cloud-based servers. The detection system 130 can instead be
operated by the user device 110, as an application external to the
browser 112. The detection system 130 may also provide the browser
extension 116 for download by the user device 110.
[0019] In general, the counterfeit URL detection system 130 applies
a trained model to content extracted from or associated with a
webpage. When applied to a set of data associated with a URL, the
model outputs a score indicating a likelihood that the URL is
counterfeit. The detection system 130 uses the score to generate an
assessment indicating either that the URL is counterfeit or not
counterfeit, and returns the assessment to the browser extension
116. The counterfeit URL detection system 130 is described further
with respect to FIG. 3.
[0020] FIG. 2 is a block diagram illustrating functional modules
within the browser extension 116, according to one embodiment. As
shown in FIG. 2, the browser extension 116 can include a browser
interface 205, a URL analyzer 210, a URL store 215, a behavior
monitor 220, and a behavior store 225. Each of the modules can
comprise computer program instructions executable by a processor,
such as a processor of the user device 110. The browser extension
116 can include additional, fewer, or different modules, and
functionality can be distributed differently between the
modules.
[0021] The browser interface 205 communicates with the browser 112
to receive URLs requested in the browser 112 and block the browser
112 from accessing URLs that are determined to be counterfeit.
[0022] The URL analyzer 210 determines whether URLs requested by
the browser 112 are counterfeit or authentic. To determine whether
a URL is counterfeit, the URL analyzer 210 can access a URL store
215 that stores a list of URLs known to be either trusted or
counterfeit. The URL store 215 can comprise a database or listing
of URLs, each mapped to an assessment of whether the URL is trusted
or counterfeit. The URL store 215 can be stored locally on the user
device 110 or on another device accessible to the URL analyzer 210.
If the received URL is listed in the URL store 215, the URL
analyzer 210 can determine whether the received URL is trusted
based on the assessment of the URL in the store 215.
[0023] In some cases, a URL that is similar but not identical to a
requested URL is stored in the URL store 215, and the URL analyzer
210 matches the requested URL to a similar stored URL based on a
heuristic. In one embodiment, the URL analyzer 210 matches the
requested URL to the URL in the store 215 if at least a portion of
the requested and stored URLs match. A matched portion of the URLs
may include at least a domain name. For example, if the requested
URL is www.example.com/sub-level, and the URL store 215 identifies
the domain www.example.com as a counterfeit URL, the URL analyzer
may determine that the requested URL is also counterfeit because it
includes at least the counterfeit domain name. In another
embodiment, the heuristic applied by the URL analyzer 210 accounts
for patterns in counterfeit and authentic URLs listed in the URL
store 215. For example, if www.example.com is assessed in the URL
store 215 as being authentic but subdomain1.example.com and
subdomain2.example.com are assessed as counterfeit, the URL
analyzer 210 may determine that subdomain3.example.com is also
likely to be counterfeit because it is more similar to the URLs
known to be counterfeit than to the authentic URL.
[0024] The URL analyzer 210 can also extract information associated
with a received URL to analyze whether the URL is counterfeit. In
some embodiments, the URL analyzer 210 extracts the information
associated with the URL if the URL is not listed in the URL store
215. In other embodiments, the URL analyzer 210 may extract the
information for some or all webpages requested by the browser 112,
even if an assessment of the URL is listed in the URL store 215.
The extracted information can include content of a webpage
referenced by the URL. For example, the URL analyzer 210 can
retrieve text from the webpage or any images on the page. The URL
analyzer 210 may additionally or alternatively extract information
from HTTP requests transmitted by the browser 112 and HTTP
responses received by the browser. For example, a header and a body
can be extracted from both the HTTP request and response. Any
information extracted by the URL analyzer 210 is sent to the
counterfeit URL detection system 130 for analysis. When an analysis
is returned by the detection system 130, the URL analyzer 210 can
add the URL and assessment to the URL store 215 and either block or
allow access to the webpage based on the assessment.
[0025] The behavior monitor 220 captures user behaviors related to
the browser 112 and counterfeit websites, and stores the user
behaviors in the behavior store 225. The user behaviors can include
a number of unique URLs requested by the user of the user device
110 in a specified period of time. In some cases, the behavior
monitor 220 can record any URL requested by the browser 112,
whether directly entered into the browser 112 by the user or
triggered by a user selection of a hyperlink in a webpage or
external application such as an email or SMS messaging application.
In other cases, the behavior monitor 220 may record only a number
of URLs that were requested in response to specified actions. For
example, the behavior monitor 220 can record a number of URLs
requested in response to a user selection of a hyperlink in an
external application, but does not record a number of URLs
requested in response to a user directly entering the URL into the
browser 112.
[0026] The user behaviors recorded by the behavior monitor 220 can
also include a number of counterfeit webpages blocked, which can be
quantified, for example, as a number of webpages blocked in a
specified period of time (e.g., three counterfeit URLs blocked in
eight hours) or as a rate relative to the number of unique URLs
requested (e.g., one counterfeit URL blocked per 100 requested
URLs). For each blocked webpage, the browser extension 116 can
record the URL of the page and information about the source of the
URL. For example, a URL source can indicate whether the user
received the URL in an email, in an SMS message, or through another
webpage. If received in a message, the behavior monitor 220 can
also record information about the sender of the message, such as an
email address or phone number of the sender. If received through
another webpage, the behavior monitor 220 can record a URL or other
identifier of the webpage.
[0027] Additional user behaviors recorded by the behavior monitor
220 can include user details associated with the user of the user
device 110. These details can include, for example, an identifier
of the user (such as a username) or of the user device 110 (such as
an IP address or MAC address), or a user-agent string.
[0028] FIG. 3 is a block diagram illustrating functional modules
within the counterfeit URL detection system 130, according to one
embodiment. As shown in FIG. 3, the detection system 130 can
include a model 305, a counterfeit assessment module 310, and a
user analytics module 315. Each of the modules can comprise
computer program instructions executable by a processor.
Furthermore, the counterfeit URL detection system 130 can include
additional, fewer, or different modules, and functionality can be
distributed differently between the modules. For example, the user
analytics module 315 may be executed by the user device 110 or a
device affiliated with an enterprise including the device 110,
rather than the counterfeit URL detection system 130.
[0029] The model 305 is a trained object representing mathematical
relationships between features related to a URL and a likelihood
that the URL is counterfeit. The model 305 can be trained using
components of webpages that are known to be counterfeit or not
counterfeit. These webpage components, including, for example, one
or more of text extracted from a webpage, an image extracted from
the webpage, HTTP request and response headers or bodies, or the
URL itself, may be grouped into a set of data representing each URL
and labeled with an assessment of the webpage's authenticity. Any
of a variety of machine learning or statistical techniques can be
applied to the labeled webpage components to generate the model
305. In some cases, different algorithms can be applied to
different types of webpage components. For example, images
extracted from the webpage can be analyzed by image object
detection and image recognition algorithms. Text can be analyzed by
a natural language processing algorithm. Threat intelligence,
either learned or received from an external provider, can
supplement these techniques.
[0030] The model 305 may be updated periodically, such as once per
month or once per year, using new sets of webpage components. For
example, the model is updated periodically in order to respond to
new techniques used by nefarious actors.
[0031] The counterfeit assessment module 310 applies the model 305
to a dataset associated with a URL to determine whether a URL is
counterfeit. The dataset, which can be transmitted to the
counterfeit assessment module 310 by the browser extension 116, may
include components of a webpage referenced by the URL, HTTP
requests and responses associated with an attempt by the browser to
display the webpage, and/or the URL itself. The counterfeit
assessment module 310 applies the model 305 to the dataset and
receives a score output by the model 305. Based on the score, the
counterfeit assessment module 310 determines whether the URL is
counterfeit.
[0032] In one embodiment, the counterfeit assessment module 310
determines whether the URL is counterfeit by comparing the score to
a threshold. If the score is greater than the threshold, the
counterfeit assessment module 310 outputs an assessment that the
URL is counterfeit. If the score is less than the threshold, the
module 310 outputs an assessment that the URL is not
counterfeit.
[0033] In another embodiment, the counterfeit assessment module 310
analyzes the score based on a threat tolerance specified by the
user of the user device 110, an administrator of an enterprise
associated with the user device 110, or another user. If an
enterprise has a low threat tolerance (because, for example, the
enterprise deals in highly sensitive data), the counterfeit
assessment module 310 sets a high threshold score. A lower
threshold score can be set for an enterprise that has a high threat
tolerance (e.g., because overly cautious URL analysis and blocking
would interrupt the workflow of the enterprise). For example, if
the model 305 outputs scores from 0 to 1, where a score of 1
indicates certainty that a URL is counterfeit, the counterfeit
assessment module 310 may set a threshold of 0.75 when an
enterprise or user has a low threat tolerance and a threshold of
0.5 when an enterprise or user has a high threat tolerance.
[0034] The user analytics module 315 receives data describing
behaviors of users that are associated with URLs and webpages, for
example as captured by the behavior monitor 220, and generates
analytics that quantify the user behaviors for one or more users.
As described above, the user behaviors can include, for example, a
number of unique URLs requested by users, a number of counterfeit
webpages blocked by the browser extension 116, and sources of the
counterfeit URLs. The user analytics module 315 analyzes the
behaviors for one or more users over a period of time and outputs a
representation of the analyzed behaviors for review by a user, such
as the user of the device 110 or an administrator of an
enterprise.
[0035] In one embodiment, the representation output by the user
analytics module 315 includes a list of any users in an enterprise
that attempted to access more than a specified number of
counterfeit URLs in a specified period of time. For example, the
user analytics module 315 identifies, based on the received user
behavior data, any user in an enterprise who attempted to use at
least five counterfeit URLs in a particular month. As another
example, the user analytics module 315 identifies any user in the
enterprise for whom counterfeit URLs constituted at least 1% of the
total number of URLs accessed by the user in a specified quarter.
The users identified by the analytics module 315 can be output to
an administrator of the enterprise to, for example, build a list of
users to whom to target training efforts.
[0036] In another embodiment, the representation output by the user
analytics module 315 identifies common sources of counterfeit URLs.
The sources identified by the analytics module 315 may be a general
category of sources through which one or more users have received a
greatest number of counterfeit URLs. For example, the analytics
module 315 may determine that 63% of all counterfeit URLs accessed
by users in an enterprise during a specified year were contained in
an email, while lower percentages of the counterfeit URLs were
accessed through SMS messages, webpages, or other sources.
Alternatively, the sources identified by the analytics module 315
may include particular originating sources who have provided the
highest number of counterfeit URLs accessed by one or more users,
or who have provided greater than a threshold number of the
counterfeit URLs accessed by the users. These particularized
sources may identify, for example, a domain name or IP address that
transmits emails containing counterfeit URLs, a telephone number
that transmits SMS messages containing counterfeit URLs, or a name
or other identifier of a user who has sent messages containing
counterfeit URLs. For example, the analytics module 315 may
determine that, of the counterfeit URLs accessed by a particular
user, a greatest number of them were provided through emails sent
from the domain @example.com.
[0037] Once a common source of counterfeit URLs has been
identified, the user analytics module 315 may generate
recommendations for reducing user attempts to access counterfeit
URLs. In some cases, the analytics module 315 combines the source
analytics with analytics identifying the users in an enterprise who
were most likely to access a counterfeit URL, providing the
enterprise with recommendations for targeted training. For example,
if the users in an enterprise who accessed the most counterfeit
URLs in a month received most of those counterfeit URLs through SMS
messages, the analytics module 315 may recommend that the
enterprise train users to identify trusted or untrusted SMS
messages. In other cases, the analytics module 315 may recommend
particular updates to a security policy, a firewall, or an email
spam filter to block messages originating from a source that has
provided a significant quantity of counterfeit URLs.
Real-Time Blocking of Counterfeit Websites
[0038] FIG. 4 is a flowchart illustrating a process 400 for
blocking user access to counterfeit websites in real-time,
according to one embodiment. The process 400 can be performed by
the user device 110, for example by executing the browser extension
116. The steps of the process 400 can include additional, fewer, or
different steps, and the steps can be performed in different
orders.
[0039] As shown in FIG. 4, the browser extension 116 receives 402 a
URL from the browser 112. The browser extension 116 can capture the
URL from the browser application 112 when the URL is requested in
the browser. In some cases, an external application calls the
browser 112 to access a URL when a user selects a hyperlink
containing the URL in the external application. For example, if the
user selects a link in an email, the email application generates a
call to the browser application 112 that contains the URL and
causes the browser 112 to access a webpage associated with the
URL.
[0040] The browser extension 116 determines 404 whether the
received URL has a match in a URL store 215. The URL store 215
stores assessments of authenticity of each of a plurality of known
URLs. The browser extension 116 may determine 404 if the received
URL matches any known URL in the store 215 by searching either for
a direct match to the received URL, or by comparing the received
URL to the known URLs using heuristics.
[0041] If the received URL is matched to a known URL in the store
215, the browser extension 116 determines 406 if the received URL
is counterfeit based on the assessment stored for the matched URL.
For example, if the URL store 215 indicates that the matched URL is
counterfeit, the browser extension 116 determines that the received
URL is also counterfeit.
[0042] If the received URL is determined 406 to be counterfeit, the
browser application 116 blocks 408 access to webpage content
referenced by the received URL. For example, the browser
application 116 transmits an instruction to the browser application
112 to not request the webpage content, to not display the webpage
content, or to stop displaying the webpage content. In some cases,
the browser application 116 redirects the browser 112 away from the
webpage associated with the URL, causing the browser to, for
example, display a page indicating that the webpage has been
blocked. The browser application 112 can also capture and record
any user behaviors related to the attempt to access the URL.
[0043] If the received URL is determined 406 to not be counterfeit,
the browser application 116 allows 410 access to content associated
with the URL. For example, the browser application 116 takes no
action to interrupt the process in the browser 112 to request and
display the webpage content referenced by the URL. User behaviors
associated with the URL can also be captured and stored in the
behavior store 225.
[0044] Returning to step 404, if the received URL does not match
any known URLs in the URL store 215, the browser application 116
extracts 412 content from a webpage referenced by the received URL.
The extracted content is sent 414 to the counterfeit URL detection
system 130 for analysis, and the browser extension 116 receives 416
an assessment of the URL from the detection system 130. The
assessment indicates whether the received URL is counterfeit. If
the assessment indicates that the URL is counterfeit 418, the
browser application 116 blocks 408 access to the webpage and
records user behavior. If the assessment indicates that the URL is
not counterfeit, the browser application 116 allows 410 the request
and records the user behavior.
[0045] FIG. 5 is a flowchart illustrating a process 500 for
analyzing whether URLs are counterfeit, according to one
embodiment. The process 500 can be performed by the counterfeit URL
detection system 130. The steps of the process 500 can include
additional, fewer, or different steps, and the steps can be
performed in different orders.
[0046] As shown in FIG. 5, the detection system 130 receives 502
webpage content from a browser extension 116 executed by a user
device 110. The received content can include content extracted from
a webpage referenced by a URL requested by a user of the user
device. User behaviors collected by the browser extension 116 can
also be transmitted to the detection system 130, either in
conjunction with the webpage content or asynchronously.
[0047] The detection system 130 applies 504 a trained model to the
received content. The model is configured to output an assessment
indicating whether a URL is counterfeit based on analysis of
webpage content associated with the URL. When the model is applied
to the received webpage content, the detection system 130 receives
an indication that the URL requested on the user device 110 is
counterfeit is or is not counterfeit.
[0048] The detection system 130 returns 506 the assessment to the
browser extension 116, which is configured to block access to the
webpage if the assessment indicates that the URL is
counterfeit.
[0049] The detection system 130 also generates 508 analytics that
quantify user behaviors related to URLs. The analytics can include,
for example, an identification of users who accessed at least a
threshold number of counterfeit URLs in a specified period of time,
or an identification of a source that provided at least a threshold
number of counterfeit URLs. The analytics can be output for display
to an administrator of the detection system 130 or provided as
feedback to a user or enterprise, for example to customize training
programs or to modify enterprise security policies.
Example Computing Device
[0050] FIG. 6 is a block diagram illustrating an example of a
processing system 600 in which at least some operations described
herein can be implemented. For example, one or more of the user
device 110 or counterfeit URL detection system 130 may be
implemented as the example processing system 600. The processing
system 600 may include one or more central processing units
("processors") 602, main memory 606, non-volatile memory 610,
network adapter 612 (e.g., network interfaces), video display 618,
input/output devices 620, control device 622 (e.g., keyboard and
pointing devices), drive unit 624 including a storage medium 626,
and signal generation device 630 that are communicatively connected
to a bus 616. The bus 616 is illustrated as an abstraction that
represents any one or more separate physical buses, point to point
connections, or both connected by appropriate bridges, adapters, or
controllers. The bus 616, therefore, can include, for example, a
system bus, a Peripheral Component Interconnect (PCI) bus or
PCI-Express bus, a HyperTransport or industry standard architecture
(ISA) bus, a small computer system interface (SCSI) bus, a
universal serial bus (USB), IIC (I2C) bus, or an Institute of
Electrical and Electronics Engineers (IEEE) standard 694 bus, also
called "Firewire."
[0051] In various embodiments, the processing system 600 operates
as part of a user device, although the processing system 600 may
also be connected (e.g., wired or wirelessly) to the user device.
In a networked deployment, the processing system 600 may operate in
the capacity of a server or a client machine in a client-server
network environment, or as a peer machine in a peer-to-peer (or
distributed) network environment.
[0052] The processing system 600 may be a server computer, a client
computer, a personal computer, a tablet, a laptop computer, a
personal digital assistant (PDA), a cellular phone, a processor, a
web appliance, a network router, switch or bridge, a console, a
hand-held console, a gaming device, a music player,
network-connected ("smart") televisions, television-connected
devices, or any portable device or machine capable of executing a
set of instructions (sequential or otherwise) that specify actions
to be taken by the processing system 600.
[0053] While the main memory 606, non-volatile memory 610, and
storage medium 626 (also called a "machine-readable medium) are
shown to be a single medium, the term "machine-readable medium" and
"storage medium" should be taken to include a single medium or
multiple media (e.g., a centralized or distributed database, and/or
associated caches and servers) that store one or more sets of
instructions 628. The term "machine-readable medium" and "storage
medium" shall also be taken to include any medium that is capable
of storing, encoding, or carrying a set of instructions for
execution by the computing system and that cause the computing
system to perform any one or more of the methodologies of the
presently disclosed embodiments.
[0054] In general, the routines executed to implement the
embodiments of the disclosure, may be implemented as part of an
operating system or a specific application, component, program,
object, module or sequence of instructions referred to as "computer
programs." The computer programs typically comprise one or more
instructions (e.g., instructions 604, 608, 628) set at various
times in various memory and storage devices in a computer, and
that, when read and executed by one or more processing units or
processors 602, cause the processing system 600 to perform
operations to execute elements involving the various aspects of the
disclosure.
[0055] Moreover, while embodiments have been described in the
context of fully functioning computers and computer systems, those
skilled in the art will appreciate that the various embodiments are
capable of being distributed as a program product in a variety of
forms, and that the disclosure applies equally regardless of the
particular type of machine or computer-readable media used to
actually effect the distribution. For example, the technology
described herein could be implemented using virtual machines or
cloud computing services.
[0056] Further examples of machine-readable storage media,
machine-readable media, or computer-readable (storage) media
include, but are not limited to, recordable type media such as
volatile and non-volatile memory devices 610, floppy and other
removable disks, hard disk drives, optical disks (e.g., Compact
Disk Read-Only Memory (CD ROMS), Digital Versatile Disks (DVDs)),
and transmission type media, such as digital and analog
communication links.
[0057] The network adapter 612 enables the processing system 600 to
mediate data in a network 614 with an entity that is external to
the processing system 600 through any known and/or convenient
communications protocol supported by the processing system 600 and
the external entity. The network adapter 612 can include one or
more of a network adaptor card, a wireless network interface card,
a router, an access point, a wireless router, a switch, a
multilayer switch, a protocol converter, a gateway, a bridge,
bridge router, a hub, a digital media receiver, and/or a
repeater.
[0058] The network adapter 612 can include a firewall which can, in
some embodiments, govern and/or manage permission to access/proxy
data in a computer network, and track varying levels of trust
between different machines and/or applications. The firewall can be
any number of modules having any combination of hardware and/or
software components able to enforce a predetermined set of access
rights between a particular set of machines and applications,
machines and machines, and/or applications and applications, for
example, to regulate the flow of traffic and resource sharing
between these varying entities. The firewall may additionally
manage and/or have access to an access control list which details
permissions including for example, the access and operation rights
of an object by an individual, a machine, and/or an application,
and the circumstances under which the permission rights stand.
[0059] As indicated above, the techniques introduced here
implemented by, for example, programmable circuitry (e.g., one or
more microprocessors), programmed with software and/or firmware,
entirely in special-purpose hardwired (i.e., non-programmable)
circuitry, or in a combination or such forms. Special-purpose
circuitry can be in the form of, for example, one or more
application-specific integrated circuits (ASICs), programmable
logic devices (PLDs), field-programmable gate arrays (FPGAs),
etc.
[0060] From the foregoing, it will be appreciated that specific
embodiments of the invention have been described herein for
purposes of illustration, but that various modifications may be
made without deviating from the scope of the invention.
Accordingly, the invention is not limited except as by the appended
claims.
* * * * *
References