Patent Application
20110004599
The present invention relates to computer networks, and in particular, to a network interface.
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.
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:
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.
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.
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.
Networks
In
One embodiment of the present invention is now illustrated with reference to
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.
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.
Capture System
One embodiment of the present invention is now described with reference to
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.
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.
One embodiment of the object assembly module 28 is now described in more detail with reference to
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 ressembler 36. Thus, in one embodiment, a flow is an ordered data stream of a single communication between a source and a destination.
The flown assembled by the reassember 36 can then is 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.
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.
In one embodiment, the object assembly module 28 outputs each flow organized by protocol, which represent the underlying objects. Referring again to
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.
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.
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.
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
Tag Data Structure
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:
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.
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.
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:
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 labeled 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).
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.
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.
Word Indexing
When a user searches over the objects captured by the capture system 22, it is desirable to make the search as fast as possible. One way to speed up searches is known as word indexing. Traditionally, word indexing involves maintaining a dictionary of words, and a list of objects associated with each word, where each object on the list contains the word. This traditional approach has several disadvantages, including non-linear growth as the number of captured objects gets large.
In one embodiment, the present invention implements a novel word indexing scheme. One embodiment of the present invention is now described with reference to
In one embodiment, the word index module 64 generates an index that can be inserted into an index field of the tag by the tag generator 68. In one embodiment, the index is a binary number that can be used to quickly eliminate object from queries. One embodiment of such an index and how it works is now described with reference to
Then, for each word 70, the bit position associated with the number that the word hashed to is “set.” Setting the appropriate bit can involve a change from 0 to 1, or 1 to 0, depending on the default bits used to populate the word index 74. In one embodiment, all bits of the word index 74 are initialized to 0 and are set to 1 is a word hashes to that bit position.
Since there are more possible words than bit positions, multiple words 70 can hash to the same number causing the same bit to be set. Thus, the fact that a certain bit is set does not guarantee that a specific word appears in the document. However, if a word hashes to a bit that is not set, that word does not appear in the document.
For example, in
There are other words not included in the document on
With the above understanding of how the word index 74 works, one embodiment of the word index module 64 is now described with reference to
The object is first processed by a text extractor 78. In one embodiment, the text extractor 78 extracts the textual content from the captured object. The text content of objects includes only textual characters without formatting or application context. The plain text extracted from the object is then provided to a tokenizer 80. The tokenizer 80 creates tokens by breaking the text over separators.
Separators can be spaces, commas, semi-colons, slashes, dashes, or any other character designated as a separator. The tokenizer 80 may apply all available separators to each extracted text file, or a specific subset of separators based on the type of the object.
The tokens, which in large part will be words in most objects, are provided, in one embodiment, to a sub-tokenizer 82. The sub-tokenizer 82 breaks some tokens into multiple tokens. It can also use an existing token to generate one or more additional tokens. In one embodiment, the sub-tokenizer 82 operates in both a content- and context-specific manner.
For example, one token created by the tokenizer 80 from an email message is the email address of the sender (e.g., billybob@reconnex.net). Since this token is highly unique, it is of greater importance than other words, such as “Ethernet.” While the “@” symbol may not be a generic separator, in the context of an email address, the information before and after the “@” symbol are highly unique and make searching emails from the sender easier. Thus, the sub-tokenizer 82 would add “billybob,” and “reconnex.net” as additional tokens.
In one embodiment, this sub-tokenization is content- and context-specific. For example, the email address sub-tokenization may only be carried out on email objects and only in the source and recipient email address context. Other such sub-tokenization contexts and content include specific alphanumeric strings (strings containing letters and/or numbers) including but not limited to credit card numbers, social security numbers, alien registration numbers or other identification numbers. In these cases, the delimiting characters (for example dashes or spaces) may be ignored such that the entire string is tokenized as a single object. In another context, these same delimiting characters may cause separate tokens to be generated.
The complete set of tokens can then proceed to a stemming module 84. The stemming module 84 can be implemented to truncate tokens that appear in various forms. For example, the tokens “confidentially,” and “confidential,” can all be stemmed to the token “confidential.”
The final list of tokens is then provided to the index generator 86. The index generator sets the bits that the tokens hashed to. Once a bit is set, another token hashing to the same bit does not change the bit setting. The word index 74 thus generated can than be inserted into the tag associated with the captured object by the tag generator 68.
In one embodiment, the word index can be inserted into the signature field described above. In another embodiment, a unique index field in the tag can contain the word index.
One embodiment of how issuing a query over captured objects is improved by using the word indexes described above is now described with reference to
The query can be received by the capture device 22 via user interface 34. The process described with reference to
In block 104, the textual search terms are tokenized in the manner described above. They may also be sub-tokenized and stemmed, as described above. In block 106, the tokens are hashed using the same hash function used to generate the word indexes, as described above. The search tokens will hash to certain bit positions of a word index. The process described with reference to blocks 104 and 106 can be carried out by a module similar or identical to the word index module 64 described above.
In block 108, a search is performed over the tag database. More specifically, the index fields of the tags associated with objects satisfying the other search criteria are examined. If all bits that the search tokens hashed to are set in a given index field of a tag, then the object associated with that tag will need to be searched. However, if not all bits that the search tokens hashed to are set in a given index field of a tag, then the object associated with that tag is, in block 110, eliminated for further consideration for the current query.
In block 112, the remaining objects are retrieved and searched for the search terms in the query. While some objects still need to be retrieved and searched, their numbers are greatly reduced by eliminating the object in block 110 that cannot contain the search terms based on their word indexes.
General Matters
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
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
Thus, a capture system and a word indexing scheme for the capture system have been described. In the forgoing description, various specific values were given names, such as “objects,” and various specific modules, such as the “words index module” and “tokenizer” 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 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.
| Number | Date | Country | |
|---|---|---|---|
| Parent | 11218167 | Aug 2005 | US |
| Child | 12873860 | US |
