U.S. patent application number 13/099516 was filed with the patent office on 2011-08-25 for object classification in a capture system.
This patent application is currently assigned to McAfee, Inc.. Invention is credited to Erik de la Iglesia, William Deninger.
Application Number | 20110208861 13/099516 |
Document ID | / |
Family ID | 35507350 |
Filed Date | 2011-08-25 |
United States Patent
Application |
20110208861 |
Kind Code |
A1 |
Deninger; William ; et
al. |
August 25, 2011 |
OBJECT CLASSIFICATION IN A CAPTURE SYSTEM
Abstract
Objects can be extracted from data flows captured by a capture
device. Each captured object can then be classified according to
content. In one embodiment, the present invention includes
determining whether a captured object is binary or textual in
nature, and classifying the captured object as one of a plurality
of textual content types based tokens found in the captured object
if the captured object is determined to be textual in nature.
Inventors: |
Deninger; William; (San
Jose, CA) ; de la Iglesia; Erik; (Mountain View,
CA) |
Assignee: |
McAfee, Inc.
|
Family ID: |
35507350 |
Appl. No.: |
13/099516 |
Filed: |
May 3, 2011 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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10876205 |
Jun 23, 2004 |
7962591 |
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13099516 |
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Current U.S.
Class: |
709/224 ;
707/803; 707/E17.044 |
Current CPC
Class: |
H04L 67/327 20130101;
H04L 67/2842 20130101; H04L 67/2819 20130101; H04L 63/12 20130101;
H04L 67/28 20130101 |
Class at
Publication: |
709/224 ;
707/803; 707/E17.044 |
International
Class: |
G06F 15/16 20060101
G06F015/16; G06F 17/30 20060101 G06F017/30 |
Claims
1.-31. (canceled)
32. A method, comprising: receiving a flow of packets in a network
environment; extracting an object from at least one of the packets;
classifying the object based on at least one signature provided
inside the object; and providing the object to an object statistics
module configured to perform a statistical calculation in order to
determine whether the object is binary or textual.
33. The method of claim 32, wherein if the object is binary, a
determination is made whether the object is encrypted.
34. The method of claim 32, wherein if the object is textual, a
determination is made about a type of text in the object.
35. The method of claim 32, wherein at least one statistical
analysis is performed in order to evaluate a frequency of bytes
contained in the object.
36. The method of claim 32, wherein if the object is determined to
be binary, then a distribution analysis is performed to determine
whether bytes in the object are uniformly distributed.
37. The method of claim 36, wherein if the bytes are distributed
uniformly, then the object is classified as being associated with
encrypted data.
38. The method of claim 36, wherein if a byte distribution is found
to be non-uniform, the object is classified using a catchall binary
unknown type.
39. The method of claim 32, further comprising: inserting an
indicator reflective of the object being classified as binary or
textual.
40. The method of claim 32, further comprising: generating a tag
data structure that includes a content field associated with the
object.
41. The method of claim 32, wherein if the object is determined to
be textual, a token database is accessed in order to statistically
analyze a presence of certain tokens in the object.
42. The method of claim 41, wherein at least some of the tokens in
the token database are reflective of either a word, a phrase, a
syntax, a grammatical notation, or a part of a word.
43. The method of claim 41, wherein the token database is organized
by content type.
44. The method of claim 41, wherein particular tokens in the token
database have a numerical weight associated thereto.
45. The method of claim 41, wherein a token analyzer is configured
to access the token database in order to sum weights for content
types associated with particular tokens of the object.
46. The method of claim 41, wherein certain tokens in the token
database are weighted differently as a function of their frequency
and as a function of their strength in an association with a
specific content type.
47. The method of claim 41, wherein the token analyzer is
configured to assign a confidence characteristic to its content
classification.
48. The method of claim 41, wherein the token analyzer is
configured to perform object classification using a Bayesian
statistical analysis, which is indicative of a probability of a
correctness of a particular classification.
49. The method of claim 41, wherein the signature is a binary
signature associated with a bit torrent.
50. An apparatus comprising: a processor; a memory element; and an
object statistics module, wherein the processor and the memory
element interact with the object statistics module such that the
apparatus is configured for; receiving a flow of packets in a
network environment; extracting an object from at least one of the
packets; classifying the object based on at least one signature
provided inside the object; and providing the object to an object
statistics module configured to perform a statistical calculation
in order to determine whether the object is binary or textual,
wherein at least one statistical analysis is performed in order to
evaluate a frequency of bytes contained in the object.
51. Logic encoded in non-transitory media that includes code for
execution and when executed by a processor operable to perform
operations comprising: receiving a flow of packets in a network
environment; extracting an object from at least one of the packets;
classifying the object based on at least one signature provided
inside the object; and providing the object to an object statistics
module configured to perform a statistical calculation in order to
determine whether the object is binary or textual, wherein if the
object is determined to be textual, a token database is accessed in
order to statistically analyze a presence of certain tokens in the
object.
Description
FIELD OF THE INVENTION
[0001] The present invention relates to computer networks, and in
particular, to a object classification.
BACKGROUND
[0002] Computer networks and systems have become indispensable
tools for modern business. Modern enterprises use such networks for
communications and for storage. The information and data stored on
the network of a business enterprise is often a highly valuable
asset. Modern enterprises use numerous tools to keep outsiders,
intruders, and unauthorized personnel from accessing valuable
information stored on the network. These tools include firewalls,
intrusion detection systems, and packet sniffer devices. However,
once an intruder has gained access to sensitive content, there is
no network device that can prevent the electronic transmission of
the content from the network to outside the network. Similarly,
there is no network device that can analyse the data leaving the
network to monitor for policy violations, and make it possible to
track down information leeks. What is needed is a comprehensive
system to capture, store, and analyse all data communicated using
the enterprises network.
SUMMARY OF THE INVENTION
[0003] Objects can be extracted from data flows captured by a
capture device. Each captured object can then be classified
according to content. In one embodiment, the present invention
includes determining whether a captured object is binary or textual
in nature, and classifying the captured object as one of a
plurality of textual content types based tokens found in the
captured object if the captured object is determined to be textual
in nature.
BRIEF DESCRIPTION OF THE DRAWINGS
[0004] The present invention is illustrated by way of example, and
not by way of limitation, in the figures of the accompanying
drawings in which like reference numerals refer to similar elements
and in which:
[0005] FIG. 1 is a block diagram illustrating a computer network
connected to the Internet;
[0006] FIG. 2 is a block diagram illustrating one configuration of
a capture system according to one embodiment of the present
invention;
[0007] FIG. 3 is a block diagram illustrating the capture system
according to one embodiment of the present invention;
[0008] FIG. 4 is a block diagram illustrating an object assembly
module according to one embodiment of the present invention;
[0009] FIG. 5 is a block diagram illustrating an object store
module according to one embodiment of the present invention;
[0010] FIG. 6 is a block diagram illustrating an example hardware
architecture for a capture system according to one embodiment of
the present invention;
[0011] FIG. 7 is a block diagram illustrating an object
classification module according to one embodiment of the present
invention; and
[0012] FIG. 8 is a flow diagram illustrating object classification
processing according to one embodiment of the present
invention.
DETAILED DESCRIPTION
[0013] Although the present system will be discussed with reference
to various illustrated examples, these examples should not be read
to limit the broader spirit and scope of the present invention.
Some portions of the detailed description that follows are
presented in terms of algorithms and symbolic representations of
operations on data within a computer memory. These algorithmic
descriptions and representations are the means used by those
skilled in the computer science arts to most effectively convey the
substance of their work to others skilled in the art. An algorithm
is here, and generally, conceived to be a self-consistent sequence
of steps leading to a desired result. The steps are those requiring
physical manipulations of physical quantities. Usually, though not
necessarily, these quantities take the form of electrical or
magnetic signals capable of being stored, transferred, combined,
compared and otherwise manipulated.
[0014] It has proven convenient at times, principally for reasons
of common usage, to refer to these signals as bits, values,
elements, symbols, characters, terms, numbers or the like. It
should be borne in mind, however, that all of these and similar
terms are to be associated with the appropriate physical quantities
and are merely convenient labels applied to these quantities.
Unless specifically stated otherwise, it will be appreciated that
throughout the description of the present invention, use of terms
such as "processing", "computing", "calculating", "determining",
"displaying" or the like, refer to the action and processes of a
computer system, or similar electronic computing device, that
manipulates and transforms data represented as physical
(electronic) quantities within the computer system's registers and
memories into other data similarly represented as physical
quantities within the computer system memories or registers or
other such information storage, transmission or display
devices.
[0015] As indicated above, one embodiment of the present invention
is instantiated in computer software, that is, computer readable
instructions, which, when executed by one or more computer
processors/systems, instruct the processors/systems to perform the
designated actions. Such computer software may be resident in one
or more computer readable media, such as hard drives, CD-ROMs,
DVD-ROMs, read-only memory, read-write memory and so on. Such
software may be distributed on one or more of these media, or may
be made available for download across one or more computer networks
(e.g., the Internet). Regardless of the format, the computer
programming, rendering and processing techniques discussed herein
are simply examples of the types of programming, rendering and
processing techniques that may be used to implement aspects of the
present invention. These examples should in no way limit the
present invention, which is best understood with reference to the
claims that follow this description.
[0016] Networks
[0017] FIG. 1 illustrates a simple prior art configuration of a
local area network (LAN) 10 connected to the Internet 12. Connected
to the LAN 102 are various components, such as servers 14, clients
16, and switch 18. There are numerous other known networking
components and computing devices that can be connected to the LAN
10. The LAN 10 can be implemented using various wireline or
wireless technologies, such as Ethernet and 802.11b. The LAN 10 may
be much more complex than the simplified diagram in FIG. 1, and may
be connected to other LANs as well.
[0018] In FIG. 1, the LAN 10 is connected to the Internet 12 via a
router 20. This router 20 can be used to implement a firewall,
which are widely used to give users of the LAN 10 secure access to
the Internet 12 as well as to separate a company's public Web
server (can be one of the servers 14) from its internal network,
i.e., LAN 10. In one embodiment, any data leaving the LAN 10
towards the Internet 12 must pass through the router 12. However,
there the router 20 merely forwards packets to the Internet 12. The
router 20 cannot capture, analyse, and searchably store the content
contained in the forwarded packets.
[0019] One embodiment of the present invention is now illustrated
with reference to FIG. 2. FIG. 2 shows the same simplified
configuration of connecting the LAN 10 to the Internet 12 via the
router 20. However, in FIG. 2, the router 20 is also connected to a
capture system 22. In one embodiment, the router 12 splits the
outgoing data stream, and forwards one copy to the Internet 12 and
the other copy to the capture system 22.
[0020] There are various other possible configurations. For
example, the router 12 can also forward a copy of all incoming data
to the capture system 22 as well. Furthermore, the capture system
22 can be configured sequentially in front of, or behind the router
20, however this makes the capture system 22 a critical component
in connecting to the Internet 12. In systems where a router 12 is
not used at all, the capture system can be interposed directly
between the LAN 10 and the Internet 12. In one embodiment, the
capture system 22 has a user interface accessible from a
LAN-attached device, such as a client 16.
[0021] In one embodiment, the capture system 22 intercepts all data
leaving the network. In other embodiments, the capture system can
also intercept all data being communicated inside the network 10.
In one embodiment, the capture system 22 reconstructs the documents
leaving the network 10, and stores them in a searchable fashion.
The capture system 22 can then be used to search and sort through
all documents that have left the network 10. There are many reasons
such documents may be of interest, including network security
reasons, intellectual property concerns, corporate governance
regulations, and other corporate policy concerns.
[0022] Capture System
[0023] One embodiment of the present invention is now described
with reference to FIG. 3. FIG. 3 shows one embodiment of the
capture system 22 in more detail. The capture system 22 includes a
network interface module 24 to receive the data from the network 10
or the router 20. In one embodiment, the network interface module
24 is implemented using one or more network interface cards (NIC),
e.g., Ethernet cards. In one embodiment, the router 20 delivers all
data leaving the network to the network interface module 24.
[0024] The captured raw data is then passed to a packet capture
module 26. In one embodiment, the packet capture module 26 extracts
data packets from the data stream received from the network
interface module 24. In one embodiment, the packet capture module
26 reconstructs Ethernet packets from multiple sources to multiple
destinations for the raw data stream.
[0025] In one embodiment, the packets are then provided the object
assembly module 28. The object assembly module 28 reconstructs the
objects being transmitted by the packets. For example, when a
document is transmitted, e.g. as an email attachment, it is broken
down into packets according to various data transfer protocols such
as Transmission Control Protocol/Internet Protocol (TCP/IP) and
Ethernet. The object assembly module 28 can reconstruct the
document from the captured packets.
[0026] One embodiment of the object assembly module 28 is now
described in more detail with reference to FIG. 4. When packets
first enter the object assembly module, they are first provided to
a reassembler 36. In one embodiment, the reassembler 36
groups--assembles--the packets into unique flows. For example, a
flow can be defined as packets with identical Source IP and
Destination IP addresses as well as identical TCP Source and
Destination Ports. That is, the reassembler 36 can organize a
packet stream by sender and recipient.
[0027] In one embodiment, the reassembler 36 begins a new flow upon
the observation of a starting packet defined by the data transfer
protocol. For a TCP/IP embodiment, the starting packet is generally
referred to as the "SYN" packet. The flow can terminate upon
observation of a finishing packet, e.g., a "Reset" or "FIN" packet
in TCP/IP. If now finishing packet is observed by the reassembler
36 within some time constraint, it can terminate the flow via a
timeout mechanism. In an embodiment using the TPC protocol, a TCP
flow contains an ordered sequence of packets that can be assembled
into a contiguous data stream by the reassembler 36. Thus, in one
embodiment, a flow is an ordered data stream of a single
communication between a source and a destination.
[0028] The flown assembled by the reassember 36 can then be
provided to a protocol demultiplexer (demux) 38. In one embodiment,
the protocol demux 38 sorts assembled flows using the TCP Ports.
This can include performing a speculative classification of the
flow contents based on the association of well-known port numbers
with specified protocols. For example, Web Hyper Text Transfer
Protocol (HTTP) packets--i.e., Web traffic--are typically
associated with port 80, File Transfer Protocol (FTP) packets with
port 20, Kerberos authentication packets with port 88, and so on.
Thus in one embodiment, the protocol demux 38 separates all the
different protocols in one flow.
[0029] In one embodiment, a protocol classifier 40 also sorts the
flows in addition to the protocol demux 38. In one embodiment, the
protocol classifier 40--operating either in parallel or in sequence
with the protocol demux 38--applies signature filters to the flows
to attempt to identify the protocol based solely on the transported
data. Furthermore, the protocol demux 38 can make a classification
decision based on port number which is subsequently overridden by
protocol classifier 40. For example, if an individual or program
attempted to masquerade an illicit communication (such as file
sharing) using an apparently benign port such as port 80 (commonly
used for HTTP Web browsing), the protocol classifier 40 would use
protocol signatures, i.e., the characteristic data sequences of
defined protocols, to verify the speculative classification
performed by protocol demux 38.
[0030] In one embodiment, the object assembly module 28 outputs
each flow organized by protocol, which represent the underlying
objects. Referring again to FIG. 3, these objects can then be
handed over to the object classification module 30 (sometimes also
referred to as the "content classifier") for classification based
on content. A classified flow may still contain multiple content
objects depending on the protocol used. For example, protocols such
as HTTP (Internet Web Surfing) may contain over 100 objects of any
number of content types in a single flow. To deconstruct the flow,
each object contained in the flow is individually extracted, and
decoded, if necessary, by the object classification module 30.
[0031] The object classification module 30 uses the inherent
properties and signatures of various documents to determine the
content type of each object. For example, a Word document has a
signature that is distinct from a PowerPoint document, or an Email
document. The object classification module 30 can extract out each
individual object and sort them out by such content types. Such
classification renders the present invention immune from cases
where a malicious user has altered a file extension or other
property in an attempt to avoid detection of illicit activity.
[0032] In one embodiment, the object classification module 30
determines whether each object should be stored or discarded. In
one embodiment, this determination is based on a various capture
rules. For example, a capture rule can indicate that Web Traffic
should be discarded. Another capture rule can indicate that all
PowerPoint documents should be stored, except for ones originating
from the CEO's IP address. Such capture rules can be implemented as
regular expressions, or by other similar means. Several embodiments
of the object classification module 30 are described in more detail
further below.
[0033] In one embodiment, the capture rules are authored by users
of the capture system 22. The capture system 22 is made accessible
to any network-connected machine through the network interface
module 24 and user interface 34. In one embodiment, the user
interface 34 is a graphical user interface providing the user with
friendly access to the various features of the capture system 22.
For example, the user interface 34 can provide a capture rule
authoring tool that allows users to write and implement any capture
rule desired, which are then applied by the object classification
module 30 when determining whether each object should be stored.
The user interface 34 can also provide pre-configured capture rules
that the user can select from along with an explanation of the
operation of such standard included capture rules. In one
embodiment, the default capture rule implemented by the object
classification module 30 captures all objects leaving the network
10.
[0034] If the capture of an object is mandated by the capture
rules, the object classification module 30 can also determine where
in the object store module 32 the captured object should be stored.
With reference to FIG. 5, in one embodiment, the objects are stored
in a content store 44 memory block. Within the content store 44 are
files 46 divided up by content type. Thus, for example, if the
object classification module determines that an object is a Word
document that should be stored, it can store it in the file 46
reserved for Word documents. In one embodiment, the object store
module 32 is integrally included in the capture system 22. In other
embodiments, the object store module can be external--entirely or
in part--using, for example, some network storage technique such as
network attached storage (NAS) and storage area network (SAN).
[0035] Tag Data Structure
[0036] In one embodiment, the content store is a canonical storage
location, simply a place to deposit the captured objects. The
indexing of the objects stored in the content store 44 is
accomplished using a tag database 42. In one embodiment, the tag
database 42 is a database data structure in which each record is a
"tag" that indexes an object in the content store 44 and contains
relevant information about the stored object. An example of a tag
record in the tag database 42 that indexes an object stored in the
content store 44 is set forth in Table 1:
TABLE-US-00001 TABLE 1 Field Name Definition MAC Address Ethernet
controller MAC address unique to each capture system Source IP
Source Ethernet IP Address of object Destination IP Destination
Ethernet IP Address of object Source Port Source TCP/IP Port number
of object Destination Port Destination TCP/IP Port number of the
object Protocol IP Protocol that carried the object Instance
Canonical count identifying object within a protocol capable of
carrying multiple data within a single TCP/IP connection Content
Content type of the object Encoding Encoding used by the protocol
carrying object Size Size of object Timestamp Time that the object
was captured Owner User requesting the capture of object (rule
author) Configuration Capture rule directing the capture of object
Signature Hash signature of object Tag Signature Hash signature of
all preceding tag fields
[0037] There are various other possible tag fields, and some
embodiments can omit numerous tag fields listed in Table 1. In
other embodiments, the tag database 42 need not be implemented as a
database, and a tag need not be a record. Any data structure
capable of indexing an object by storing relational data over the
object can be used as a tag data structure. Furthermore, the word
"tag" is merely descriptive, other names such as "index" or
"relational data store," would be equally descriptive, as would any
other designation performing similar functionality.
[0038] The mapping of tags to objects can, in one embodiment, be
obtained by using unique combinations of tag fields to construct an
object's name. For example, one such possible combination is an
ordered list of the Source IP, Destination IP, Source Port,
Destination Port, Instance and Timestamp. Many other such
combinations including both shorter and longer names are possible.
In another embodiment, the tag can contain a pointer to the storage
location where the indexed object is stored.
[0039] The tag fields shown in Table 1 can be expressed more
generally, to emphasize the underlying information indicated by the
tag fields in various embodiments. Some of these possible generic
tag fields are set forth in Table 2:
TABLE-US-00002 TABLE 2 Field Name Definition Device Identity
Identifier of capture device Source Address Origination Address of
object Destination Address Destination Address of object Source
Port Origination Port of object Destination Port Destination Port
of the object Protocol Protocol that carried the object Instance
Canonical count identifying object within a protocol capable of
carrying multiple data within a single connection Content Content
type of the object Encoding Encoding used by the protocol carrying
object Size Size of object Timestamp Time that the object was
captured Owner User requesting the capture of object (rule author)
Configuration Capture rule directing the capture of object
Signature Signature of object Tag Signature Signature of all
preceding tag fields
[0040] For many of the above tag fields in Tables 1 and 2, the
definition adequately describes the relational data contained by
each field. For the content field, the types of content that the
object can be labelled as are numerous. Some example choices for
content types (as determined, in one embodiment, by the object
classification module 30) are JPEG, GIF, BMP, TIFF, PNG (for
objects containing images in these various formats); Skintone (for
objects containing images exposing human skin); PDF, MSWord, Excel,
PowerPoint, MSOffice (for objects in these popular application
formats); HTML, WebMail, SMTP, FTP (for objects captured in these
transmission formats); Telnet, Rlogin, Chat (for communication
conducted using these methods); GZIP, ZIP, TAR (for archives or
collections of other objects); Basic_Source, C++_Source, C_Source,
Java_Source, FORTRAN_Source, Verilog_Source, VHDL_Source,
Assembly_Source, Pascal_Source, Cobol_Source, Ada_Source,
Lisp_Source, Perl_Source, XQuery_Source, Hypertext Markup Language,
Cascaded Style Sheets, JavaScript, DXF, Spice, Gerber, Mathematica,
Matlab, AllegroPCB, ViewLogic, TangoPCAD, BSDL, C_Shell, K_Shell,
Bash_Shell, Bourne_Shell, FTP, Telnet, MSExchange, POP3, RFC822,
CVS, CMS, SQL, RTSP, MIME, PDF, PS (for source, markup, query,
descriptive, and design code authored in these high-level
programming languages); C Shell, K Shell, Bash Shell (for shell
program scripts); Plaintext (for otherwise unclassified textual
objects); Crypto (for objects that have been encrypted or that
contain cryptographic elements); Englishtext, Frenchtext,
Germantext, Spanishtext, Japanesetext, Chinesetext, Koreantext,
Russiantext (any human language text); Binary Unknown, ASCII
Unknown, and Unknown (as catchall categories).
[0041] The signature contained in the Signature and Tag Signature
fields can be any digest or hash over the object, or some portion
thereof. In one embodiment, a well-known hash, such as MD5 or SHA1
can be used. In one embodiment, the signature is a digital
cryptographic signature. In one embodiment, a digital cryptographic
signature is a hash signature that is signed with the private key
of the capture system 22. Only the capture system 22 knows its own
private key, thus, the integrity of the stored object can be
verified by comparing a hash of the stored object to the signature
decrypted with the public key of the capture system 22, the private
and public keys being a public key cryptosystem key pair. Thus, if
a stored object is modified from when it was originally captured,
the modification will cause the comparison to fail.
[0042] Similarly, the signature over the tag stored in the Tag
Signature field can also be a digital cryptographic signature. In
such an embodiment, the integrity of the tag can also be verified.
In one embodiment, verification of the object using the signature,
and the tag using the tag signature is performed whenever an object
is presented, e.g., displayed to a user. In one embodiment, if the
object or the tag is found to have been compromised, an alarm is
generated to alert the user that the object displayed may not be
identical to the object originally captured.
[0043] Object Classification
[0044] One embodiment of the object classification module 30 is now
described in more detail with reference to FIG. 7. As described
above, in one embodiment, the output of the object assembly module
28 are flows classified by protocol. In one embodiment, the object
classification module 30 includes a number of protocol handlers 62
designed to extract the objects from a classified flow.
[0045] For some protocols, such as HTTP, an off-the-shelf protocol
handler can be used. For other protocols, the creator of the
protocol may provide a protocol handler. Some protocol handlers 62
are designed specially for the capture system 22. In one
embodiment, a protocol handlers 62 is included to extract objects
from any known transmission protocol, such as HTTP and SMTP. The
protocol handlers 62 and object extraction can also be implemented
in the object assembly module 28, or in any other module prior to
object classification.
[0046] Where the object assembly module 28 has been unable to
identify the protocol of the flow, the flow is provided to an
"unknown protocol handler," included in the list of protocol
handlers 62. In one embodiment, the unknown protocol handler
extracts the objects contained in the unidentified flow in the
absence of a known protocol. In one embodiment, the unknown
protocol handler classifies the entire received flow as a single
object. For example, classifying the entire unknown flow as one
object can address the difficulty associated with classifying FTP
data flows. Other embodiments for the operation of the unknown
protocol handler are described further below.
[0047] In one embodiment, the extracted object (or objects) is
input for the binary signature module 64. As explained above, the
binary signature module 64 attempts to classify an object based on
binary signatures found inside the object. Binary signatures result
from the content encapsulating software operating in some unique
manner.
[0048] Binary signatures may be inserted on purpose of by
happenstance. For example, the binary signature of a Bit Torrent
object is the string "BitTorrent" seen at the very beginning of the
object. Similarly, all Microsoft Office documents begin with a
32-bit Microsoft identifier based on which each office document can
be classified. As another example, JPEG images contain the string
"JFIF" at the ninth byte of the object, and the twelfth byte of the
object is 0x30 in hexadecimal notation.
[0049] Binary signatures may be collected from various sources,
such as UNIX "Magic Files," or additional research and observation.
In one embodiment, the signature database containing the signatures
of known content types is updated regularly. Signatures can change
or become obsolete, while new signatures may be added to known
content types or because of new content types.
[0050] In one embodiment, if the binary signature module 64 is able
to classify the object by content, then the content classification
is inserted into the "Content" field of the tag data structure set
forth above. If, however, the binary signature module 64 is unable
to classify the object, i.e., the object did not match any known
signatures, then the object is provided to the object statistics
module 66.
[0051] In one embodiment, the object statistics module 66 performs
various statistical calculations on the object and reaches one or
more conclusions based on the results of these calculations.
Specifically, in one embodiment, the object statistics module 66
determines whether the object is binary or textual in nature, if
binary, whether it is encrypted, and, if textual, what type of text
the object contains.
[0052] In one embodiment, one statistical analysis performed by the
object statistics module 66 calculates the frequency of the bytes
contained in the object. In one embodiment, if all 256 possible
bytes occur with statistically even frequency, then the object is
processed further as a binary object. If, however, certain bytes
associated with textual formats--such as ASCII, extended ASCII, or
Unicode)--are seen with elevated frequencies, then the object is
processed as a text object.
[0053] In one embodiment, if the object is determined to be binary
data, then the object statistics module 66 performs a distribution
analysis (e.g., calculating the variance of the byte distibution)
to determine whether the bytes are uniformly distributed or not. In
one embodiment, if the bytes are distributed uniformly (to a
statistically significant degree), then the object statistics
module 66 classifies the object as content type "crypto," i.e.,
encrypted data, since most encrypted data appears randomized. In
one embodiment, if the byte distribution is found to be
non-uniform, the object is classified using the catchall
"Binary_Unknown" type. The appropriate classification is then
inserted into the tag.
[0054] In one embodiment, if the object is determined to be text
(e.g., ASCII), then the object statistics module 66 accesses a
token database 68 to statistically analyze whether and/or how many
times each token appears in the object. A token may be a word, a
phrase, a part of a word, grammatical notations, patterns, syntax,
and any other textual data. Tokens may vary in size, but will
generally be relatively small, usually between 3 and 12 bytes in
length. The tokens need not be stored in a token database 68, any
appropriate storage scheme and data structure can be used.
[0055] The statistical information associated with the tokens is
provided, in one embodiment, to the token analyzer 70, which
classifies the object as one of a number of various text types
using the information. Since various textual documents include
different types of syntax, grammar, and words, it is possible to
classify text objects using such tokens. For example, certain
phrases--such as "is a", "the", "and"--appear more regularly in
English language text than text in other languages. Similarly,
certain tokens--such as "++", "for ("--appear often in certain
programming languages.
[0056] In one embodiment, the possible textual content types
include Englishtext, Frenchtext, Germantext, Spanishtext,
Japanesetext, Chinesetext, Koreantext, Russiantext, (i.e., text
from any specific language or a catchall Languagetext category) and
various programming language, markup language, query language, and
other computer language source code, including Basic_Source,
C++_Source, C_Source, Java_Source, FORTRAN_Source, Verilog_Source,
VHDL_Source, Assembly_Source, Pascal_Source, Cobol_Source,
Ada_Source, Lisp_Source, Perl_Source, XQuery_Source, Hypertext
Markup Language, Cascaded Style Sheets, JavaScript, DXF, Spice,
Gerber, Mathematica, Matlab, AllegroPCB, ViewLogic, TangoPCAD,
BSDL, C_Shell, K_Shell, Bash_Shell, Bourne_Shell, FTP, Telnet,
MSExchange, POP3, RFC822, CVS, CMS, SQL, RTSP, MIME, PDF, PS, and
Stockdata.
[0057] In one embodiment, the tokens in the token database 68 are
organized by content type. In other words, each possible content
type has tokens associated with it. Furthermore, each token has a
numerical weight associated with it. In one embodiment, the token
analyzer 70 accesses the token database 68, and calculates a raw
number associated with each content type by summing the weights of
the tokens found in the object, counting each instance of tokens
found more than once. The token analyzer 70 can then classify the
object according to the content type with the highest numerical
value.
[0058] In one embodiment, the tokens in the token database 68 are
weighted differently as a function of their frequency and the
strength of their association with the specific content type. For
example, a common English language word will have a lower weight
than a syntax that is highly specific to a certain programming
language, such as C++, or other documentation language, such as
Verilog.
[0059] In one embodiment, the token analyzer 70 assigns a
confidence to its classification. For example, if the token
summation for Verilog tokens present in an object is twice the
total of other content type tokens, then the confidence in a
Veriolog classification is relatively high. If, however, two or
more content types have token sums that are closer together, the
confidence that the content type with the highest token sum is the
correct classification is lower. The confidence can be expressed
numerically, by ranger, or as a percentage.
[0060] In one embodiment, the token analyzer 70 performs object
classification using Bayesian statistics, which naturally indicate
the probability of the correctness of the classification. For
Bayesian analysis the token analyzer only needs to know which
tokens are present in the object, but not how many times each token
was observed in the object. Based on this input, Bayesian
statistics can provide the probability of the object being each of
the content types. The highest probability content type can be the
classification received by the object.
[0061] The probability that the object is the classified content
type provided by Bayesian statistics can be converted to, or used
as, a confidence in the object classification. In one embodiment,
where two (or more) content types are close in probability, both as
stored in the content field of the tag, with the appropriate
probabilities.
[0062] The various embodiments of the object classification method
described above have been described in terms of functional modules
carrying out the various actions required by each embodiment.
However, the modular architecture shown in FIG. 7 is just one
example architecture for implementing object classification. Thus,
one embodiment demonstrating object classification without any
specific architecture is now described with reference to FIG.
8.
[0063] The input for object classification remains the captured,
assembled, and classified flow of packets. In block 102, a
determination is made as to whether the protocol carrying the flow
is known. If yes, then in block 104 the appropriate protocol
handler associated with the known protocol (e.g., HTTP) is called
to extract one or more objects from the flow.
[0064] If, on the other hand, the protocol is not determinable or
unknown (e.g., an FTP data flow), then in block 106 the unknown
protocol handler is called to extract the objects from the flow. In
one embodiment, the unknown protocol handler outputs the entire
flow as an object. In another embodiment the unknown protocol
handler employs methods similar to those discussed with reference
to object classification to extract objects from the unclassifiable
flow.
[0065] In one embodiment, the unknown protocol handler traverses
the unknown flow looking for statistically strong binary
signatures. If a probable binary signature is found the object
embedded in the unknown flow can be simultaneously extracted and
classified based on the binary signature without a priori knowledge
of the underlying protocol of the flow.
[0066] In one embodiment, the unknown protocol handler is
configured to identify textual domains--also referred to as ASCII
domains--which are regions of the flow identified by a strong ASCII
statistical components. If a textual domain is identified in the
unclassified flow, the token classification method described above
may be employed to extract and classify the textual object content
contained in the flow.
[0067] After one or more objects are extracted, processing can
proceed object by object, or in parallel on a per object basis. In
block 108, an attempt is made to classify the object using binary
signatures, as set forth above. If in block 110 it is determined
that a binary signature has been found, then the object is
classified based on the binary signature in block 112 and the
processing terminates.
[0068] On the other hand, if binary signature classification fails,
then in block 114 statistical analysis is performed on the object.
This can include, but is not limited to, byte analysis (e.g., how
many times each possible byte occurred in the object), byte
distribution analysis (e.g., how were the bytes distributed across
the object), token presence analysis (e.g., what known tokens were
found in the object), and token frequency analysis (e.g., how many
times each token was found in the object).
[0069] In block 116 a decision is made as to whether the object is
binary or textual in nature. For example, if ASCII character bytes
occur more frequently than other bytes, the object may be
determined to be textual in nature. However, if all bytes occur
with approximately even frequency, then the object is probably
binary. If the object is binary, then in block 118 a determination
is made as to whether the byte distribution is uniform, based on
the analysis performed in block 114.
[0070] If the distribution of the bytes throughout the object is
uniform--defined for example as the variance or standard deviation
of the bytes being below three sigma (3 .sigma.) or some other
threshold--then the object is classified as a cryptographic object
in block 120. In other words, the object is determined to include
content that is encrypted by some cryptographic method, and the
processing terminates. If, the byte distribution is found to be
non-uniform (i.e., non-random), then the object is classified as a
binary unknown object in block 122, and the processing
terminates.
[0071] If, in block 116 the object was determined to be textual in
nature, then token analysis is performed on the object in block
124. Token analysis can include calculating totals of token weights
found in the object, performing Bayesian statistics of content
types based on tokens present in the object, or any other
token-based method of determining content type. Based on the
calculations performed in block 124, the object is classified as
some textual content type in block 126.
[0072] In block 128, the confidence of the classification of block
126 is calculated. The confidence may be based on a Bayesian
statistic, an comparison of weight sums of other content types, or
some other statistical method. The object classification processing
then terminates. The object classification derived as a result of
the processing can then be used to populate a tag describing the
object, or can be associated with the object in some other way,
e.g., in a database.
[0073] General Matters
[0074] In several embodiments, the capture system 22 has been
described above as a stand-alone device. However, the capture
system of the present invention can be implemented on any appliance
capable of capturing and analyzing data from a network. For
example, the capture system 22 described above could be implemented
on one or more of the servers 14 or clients 16 shown in FIG. 1. The
capture system 22 can interface with the network 10 in any number
of ways, including wirelessly.
[0075] In one embodiment, the capture system 22 is an appliance
constructed using commonly available computing equipment and
storage systems capable of supporting the software requirements. In
one embodiment, illustrated by FIG. 6, the hardware consists of a
capture entity 46, a processing complex 48 made up of one or more
processors, a memory complex 50 made up of one or more memory
elements such as RAM and ROM, and storage complex 52, such as a set
of one or more hard drives or other digital or analog storage
means. In another embodiment, the storage complex 52 is external to
the capture system 22, as explained above. In one embodiment, the
memory complex stored software consisting of an operating system
for the capture system device 22, a capture program, and
classification program, a database, a filestore, an analysis engine
and a graphical user interface.
[0076] Thus, a capture system and an object classification
procedure have been described. In the forgoing description, various
specific values were given names, such as "objects," and various
specific modules, such as the "object statistics module" and "token
database" have been described. However, these names are merely to
describe and illustrate various aspects of the present invention,
and in no way limit the scope of the present invention.
Furthermore, various modules, such as the binary signature module
64 and the token analyzer 70 in FIG. 7, can be implemented as
software or hardware modules, or without dividing their
functionalities into modules at all. The present invention is not
limited to any modular architecture either in software or in
hardware, whether described above or not.
* * * * *