Patent Publication Number: US-2013246338-A1

Title: System and method for indexing a capture system

Description:
CLAIM TO PRIORITY 
     This application claims the benefit of U.S. Provisional Application No. 60/845,002 filed Sep. 15, 2006. 
    
    
     FIELD OF THE INVENTION 
     The present invention relates to computer networks, and in particular, to registering documents in a computer network. 
     BACKGROUND 
     Computer networks and systems have become indispensable tools for modem 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. Modem 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. 
       FIG. 1  illustrates a simple prior art configuration of a local area network (LAN)  100  connected to the Internet  102 . Connected to LAN  100  are various components, such as servers  104 , clients  106 , and switch  108 . Numerous other networking components and computing devices may be connected to the LAN  100 . The LAN  100  may be implemented using various wireline (e.g., Ethernet) or wireless technologies (e.g., IEEE 802.11x). LAN  100  could also be connected to other LANs. 
     In this prior configuration, LAN  100  is connected to the Internet  102  via a router  110 . Router  110  may be used to implement a firewall. Firewalls are used to try to provide users of LANS with secure access to the Internet as well as to provide a separation of a public Web server (e.g., one of the servers  104 ) from an internal network (e.g., LAN  100 ). Data leaving LAN  100  and going to the Internet  102  passes through router  110 . Router  110  simply forwards packets as is from LAN  100  to the Internet  102 . 
     Once an intruder has gained access to sensitive content inside a LAN such as LAN  100 , presently there is no network device that can prevent the electronic transmission of the content from the network (e.g., LAN  100 ) to outside the network. Similarly, there is no network device that can analyze the data leaving the network in order to monitor for policy violations, and/or make it possible to track down information leaks. What is needed is a comprehensive system to capture, store, and analyze data communicated using the enterprise&#39;s network. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       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: 
         FIG. 1  is a block diagram illustrating a computer network connected to the Internet; 
         FIG. 2  is a block diagram illustrating one configuration of a capture system according to one embodiment of the present invention; 
         FIG. 3  is a block diagram illustrating the capture system according to one embodiment of the present invention; 
         FIG. 4  is a block diagram illustrating an object assembly module according to one embodiment of the present invention; 
         FIG. 5  is a block diagram illustrating an object store module according to one embodiment of the present invention; 
         FIG. 6  is a block diagram illustrating a document registration system according to one embodiment of the present invention; 
         FIG. 7  is a block diagram illustrating registration module according to one embodiment of the present invention; and 
         FIG. 8  illustrates an embodiment of the flow of the operation of a registration module; 
         FIG. 9  is a flow diagram illustrating an embodiment of a flow to generate signatures; 
         FIG. 10  is a flow diagram illustrating an embodiment of changing tokens into document signatures; 
         FIG. 11  illustrates an embodiment of a registration engine that generates signatures for documents; 
         FIG. 12  illustrates an exemplary embodiment of a network capture device; 
         FIG. 13  illustrates an exemplary indexing and searching flow; 
         FIG. 14  illustrates an example of a keyword and metadata index at a particular point in time; 
         FIG. 15  illustrates a simplified exemplary querying flow using metadata and keyword indexing; and 
         FIG. 16  shows an embodiment of a computing system (e.g., a computer). 
     
    
    
     DETAILED DESCRIPTION 
     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 art of computer science to most effectively convey the substance of their work to others skilled in the art. An algorithm is 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. Keep 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”, etc., 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&#39;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. 
     Exemplary Networks 
     As described earlier with respect to  FIG. 1 , the router  110  of the prior art simply routes packets to and from a network and the Internet. While the router may log that a transaction has occurred (i.e., packets have been routed), it does not capture, analyze, or store the content contained in the packets. 
       FIG. 2  illustrates an embodiment of a system utilizing a capture device. In  FIG. 2 , the router  210  is also connected to a capture system  200  in addition to the Internet  202  and LAN  212 . Generally, the router  210  transmits the outgoing data stream to the Internet  202  and a copy of that stream to the capture system  200 . The router  210  may also send incoming data to the capture system  200  and LAN  212 . 
     However, other configurations are possible. For example, the capture system  200  may be configured sequentially in front of or behind the router  210 . In systems where a router is not used, the capture system  200  is located between the LAN  212  and the Internet  202 . In other words, if a router is not used the capture system  200  forwards packets to the Internet  202 . In one embodiment, the capture system  200  has a user interface accessible from a LAN-attached device such as a client  206 . 
     The capture system  200  intercepts data leaving a network such as LAN  212 . In an embodiment, the capture system also intercepts data being communicated internally to a network such as LAN  212 . The capture system  200  reconstructs the documents leaving the network  100  and stores them in a searchable fashion. The capture system  200  is then used to search and sort through all documents that have left the network  100 . 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. Exemplary documents include, but are not limited to, Microsoft Office documents (such as Word, Excel, etc.), text files, images (such as JPEG, BMP, GIF, PNG, etc.), Portable Document Format (PDF) files, archive files (such as GZIP, ZIP, TAR, JAR, WAR, RAR, etc.), email messages, email attachments, audio files, video files, source code files, executable files, etc. 
     Capture System 
       FIG. 3  shows an embodiment of a capture system in greater detail. A capture system (such as capture system  200  or  312 ) may also be referred to as a content analyzer, content/data analysis system, or other similar name. For simplicity, the capture system has been labeled as capture system  300 . However, the discussion regarding capture system  300  is equally applicable to capture system  200 . A network interface module  300  receives (captures) data, such as data packets, from a network or router. Exemplary network interface modules  300  include network interface cards (NICs) (for example, Ethernet cards). More than one NIC may be present in a capture system. 
     This captured data is passed from the network interface module  300  to a packet capture module  302  which extracts packets from the captured data. The packet capture module  302  may extract packets from streams with different sources and/or destinations. One such case is asymmetric routing where a packet sent from source “A” to destination “B” travels along a first path and responses sent from destination “B” to source “A” travel along a different path. Accordingly, each path could be a separate “source” for the packet capture module  302  to obtain packets. Additionally, packet data may be extracted from a packet by removing the packet&#39;s header and checksum. 
     When an object is transmitted, such 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”), UDP, HTTP, etc. An object assembly module  304  reconstructs the original or a reasonably equivalent document from the captured packets. For example, a PDF document broken down into packets before being transmitted from a network is reassembled to form the original, or reasonable equivalent of the, PDF from the captured packets associated with the PDF document. A complete data stream is obtained by reconstruction of multiple packets. The process by which a packet is created is beyond the scope of this application. 
       FIG. 4  illustrates a more detailed embodiment of object assembly module  304 . This object assembly module includes a re-assembler  400 , protocol demultiplexer (“demux”)  402 , and a protocol classifier  404 . Packets entering the object assembly module  304  are provided to the re-assembler  400 . The re-assembler  400  groups (assembles) the packets into at least one unique flow. A TCP/IP flow contains an ordered sequence of packets that may be assembled into a contiguous data stream by the re-assembler  400 . An exemplary flow includes packets with an identical source IP and destination IP address and/or identical TCP source and destination ports. In other words, the re-assembler  400  assembles a packet stream (flow) by sender and recipient. Thus, a flow is an ordered data stream of a single communication between a source and a destination. In an embodiment, a state machine is maintained for each TCP connection which ensures that the capture system has a clear picture of content moving across every connection. 
     The re-assembler  400  begins a new flow upon the observation of a starting packet. This starting packet is normally defined by the data transfer protocol being used. For example, the starting packet of a TCP flow is a “SYN” packet. The flow terminates upon observing a finishing packet (e.g., a “Reset” or “FIN” packet in TCP/IP) or via a timeout mechanism if the finished packing is not observed within a predetermined time constraint. 
     A flow assembled by the re-assembler  400  is provided to a protocol demultiplexer (“demux”)  402 . The protocol demux  402  sorts assembled flows using ports, such as TCP and/or UDP ports, by performing speculative classification of the flow&#39;s contents based on the association of well known port numbers with specified protocols. For example, because web Hyper Text Transfer Protocol (HTTP) packets, such as, Web traffic packets, are typically associated with TCP port  80 , packets that are captured over TCP port  80  are speculatively classified as being HTTP. Examples of other well known ports include TCP port  20  (File Transfer Protocol (“FTP”)), TCP port  88  (Kerberos authentication packets), etc. Thus, the protocol demux  402  separates the flows by protocols. 
     A protocol classifier  404  further sorts flows. The protocol classifier  404  (operating either in parallel or in sequence to the protocol demux  402 ) applies signature filters to a flow to identify the protocol based solely on the transported data. The protocol classifier  404  uses a protocol&#39;s signature(s) (i.e., the characteristic data sequences of a defined protocol) to verify the speculative classification performed by the protocol demux  402 . If the protocol classifier  404  determines that the speculative classification is incorrect it overrides it. For example, if an individual or program attempted to masquerade an illicit communication (such as file sharing) using an apparently benign port (for example, TCP port  80 ), the protocol classifier  404  would use the HTTP protocol signature(s) to verify the speculative classification performed by protocol demux  402 . 
     Protocol classification helps identify suspicious activity over non-standard ports. A protocol state machine is used to determine which protocol is being used in a particular network activity. This determination is made independent of the port or channel on which the protocol is active. As a result, the capture system recognizes a wide range of protocols and applications, including SMTP, FTP, HTTP, P2P, and proprietary protocols in client-server applications. Because protocol classification is performed independent of which port number was used during transmission, the capture system monitors and controls traffic that may be operating over non-standard ports. Non-standard communications may indicate that an enterprise is at risk from spyware, adware, or other malicious code, or that some type of network abuse or insider threat may be occurring. 
     The object assembly module  304  outputs each flow, organized by protocol, representing the underlying objects being transmitted. These objects are passed to the object classification module  306  (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, a single flow using HTTP may contain over 100 objects of any number of content types. To deconstruct the flow, each object contained in the flow is individually extracted and decoded, if necessary, by the object classification module  306 . 
     The object classification module  306  uses the inherent properties and/or signature(s) 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. The object classification module  306  extracts each object and sorts them according to content type. This classification prevents the transfer of a document whose file extension or other property has been altered. For example, a Word document may have its extension changed from .doc to .dock but the properties and/or signatures of that Word document remain the same and detectable by the object classification module  306 . In other words, the object classification module  306  functions beyond simple extension filtering. 
     According to an embodiment, a capture system uses one or more of six mechanisms for classification: 1) content signature; 2) grammar analysis; 3) statistical analysis; 4) file classification; 5) document biometrics; and 6) concept maps. Content signatures are used to look for predefined byte strings or text and number patterns (i.e., Social Security numbers, medical records, and bank accounts). When a signature is recognized, it becomes part of the classification vector for that content. While beneficial when used in combination with other metrics, signature matching alone may lead to a high number of false positives. 
     Grammar analysis determines if an object&#39;s content is in a specific language and filters accordingly based on this information. Various types of content have their own grammar or syntax. For example, “C” source code uses “if/then” grammar. Legal documents, resumes, and earnings results also have a particular grammar. Grammar analysis also enables an organization to detect the presence of non-English language-based content on their network. 
     File classification identifies content types regardless of the extensions applied to the file or compression. The file classification mechanism looks for specific file markers instead of relying on normal telltale signs such as .xls or .pdf. 
     Document biometrics identifies sensitive data even if the data has been modified. Document biometrics recognizes content rich elements in files regardless of the order or combination in which they appear. For example, a sensitive Word document may be identified even if text elements inside the document or the file name itself have been changed. Excerpts of larger files, e.g., a single column exported from an Excel spreadsheet containing Social Security numbers, may also be identified. 
     Document biometrics takes “snapshots” of protected documents in order to build a signature set for protecting them. In an embodiment, document biometrics distinguishes between public and confidential information within the same document. 
     Statistical analysis assigns weights to the results of signature, grammar, and biometric analysis. That is, the capture system tracks how many times there was a signature, grammar, or biometric match in a particular document or file. This phase of analysis contributes to the system&#39;s overall accuracy. 
     Concept maps may be used to define and track complex or unique content, whether at rest, in motion, or captured. Concept maps are based on combinations of data classification mechanisms and provide a way to protect content using compound policies. The object classification module  306  may also determine whether each object should be stored or discarded. This determination is based on definable capture rules used by the object classification module  306 . For example, a capture rule may indicate that all Web traffic is to be discarded. Another capture rule may indicate that all PowerPoint documents should be stored except for ones originating from the CEO&#39;s IP address. Such capture rules are implemented as regular expressions or by other similar means. 
     Capture rules may be authored by users of a capture system. The capture system may also be made accessible to any network-connected machine through the network interface module  300  and/or user interface  310 . In one embodiment, the user interface  310  is a graphical user interface providing the user with easy access to the various features of the capture system  312 . For example, the user interface  310  may provide a capture rule authoring tool that allows any capture rule desired to be written. These rules are then applied by the object classification module  306  when determining whether an object should be stored. The user interface  310  may also provide pre-configured capture rules that the user selects from along with an explanation of the operation of such standard included capture rules. Generally, by default, the capture rule(s) implemented by the object classification module  306  captures all objects leaving the network that the capture system is associated with. 
     If the capture of an object is mandated by one or more capture rules, the object classification module  306  may determine where in the object store module  308  the captured object should be stored.  FIG. 5  illustrates an embodiment of object store module  308 . Accordingly to this embodiment, the object store includes a tag database  500  and a content store  502 . Within the content store  502  are files  504  grouped by content type. For example, if object classification module  306  determines that an object is a Word document that should be stored, it can store it in the file  504  reserved for Word documents. The object store module  506  may be internal to a capture system or external (entirely or in part) using, for example, some network storage technique such as network attached storage (NAS), storage area network (SAN), or other database. 
     In an embodiment, the content store  502  is a canonical storage location that is simply a place to deposit the captured objects. The indexing of the objects stored in the content store  502  is accomplished using a tag database  500 . The tag database  500  is a database data structure in which each record is a “tag” that indexes an object in the content store  502  and contains relevant information about the stored object. An example of a tag record in the tag database  500  that indexes an object stored in the content store  502  is set forth in Table 1: 
     
       
         
           
               
               
             
               
                 TABLE 1 
               
               
                   
               
               
                 Field Name 
                 Definition (Relevant Information) 
               
               
                   
               
             
            
               
                 MAC Address 
                 NIC MAC address 
               
               
                 Source IP 
                 Source IP address of object 
               
               
                 Destination IP 
                 Destination IP address of object 
               
               
                 Source Port 
                 Source port number of object 
               
               
                 Destination Port 
                 Destination port number 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 
               
               
                   
                 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 (possibly rule 
               
               
                   
                 author) 
               
               
                 Configuration 
                 Capture rule directing the capture of object 
               
               
                 Signature 
                 Hash signature of object 
               
               
                 Tag Signature 
                 Hash signature of all preceding tag fields 
               
               
                   
               
            
           
         
       
     
     There are various other possible tag fields and some tag fields listed in Table 1 may not be used. In an embodiment, the tag database  500  is not implemented as a database and another data structure is used. 
     The mapping of tags to objects may be obtained by using unique combinations of tag fields to construct an object&#39;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. A tag may 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: 
     
       
         
           
               
               
             
               
                 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 
               
               
                   
               
            
           
         
       
     
     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. 
     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 to perform searches over the tag database instead of the content store, since the content store will generally be stored on disk and is far more costly both in terms of time and processing power to search then a database. 
     The objects and tags stored in the object store module  308  may be interactively queried by a user via the user interface  310 . In one embodiment, the user interface interacts with a web server (not shown) to provide the user with Web-based access to the capture system  312 . The objects in the object store module  308  are searchable for specific textual or graphical content using exact matches, patterns, keywords, and/or various other attributes. 
     For example, the user interface  310  may provide a query-authoring tool (not shown) to enable users to create complex searches of the object store module  308 . These search queries are provided to a data mining engine (not shown) that parses the queries to the object store module. For example, tag database  500  may be scanned and the associated object retrieved from the content store  502 . Objects that matched the specific search criteria in the user authored query are counted and/or displayed to the user by the user interface  310 . 
     Searches may be scheduled to occur at specific times or at regular intervals. The user interface  310  may provide access to a scheduler (not shown) that periodically executes specific queries. Reports containing the results of these searches are made available to the user at runtime or at a later time such as generating an alarm in the form of an e-mail message, page, system log, and/or other notification format. 
     A user query for a pattern is generally in the form of a regular expression. A regular expression is a string that describes or matches a set of strings, according to certain syntax rules. There are various well-known syntax rules such as the POSIX standard regular expressions and the PERL scripting language regular expressions. Regular expressions are used by many text editors and utilities to search and manipulate bodies of text based on certain patterns. Regular expressions are well-known in the art. For example, according to one syntax (Unix), the regular expression 4/d{15} means the. digit “4” followed by any fifteen digits in a row. This user query would return all objects containing such a pattern. 
     Certain useful search categories cannot be defined well by a single regular expression. As an example, a user may want to query all emails containing a credit card number. Various credit card companies used different numbering patterns and conventions. A card number for each company can be represented by a regular expression. However, the concept of credit card number can be represented by a union of all such regular expressions. 
     For such categories, the concept of attribute is herein defined. An attribute, in one embodiment, represents a group of one or more regular expressions (or other such patterns). The term “attribute” is merely descriptive, such concept could just as easily be termed “category,” “regular expression list,” or any other descriptive term. 
     Generally, a capture system has been described above as a stand-alone device. However, capture systems may be implemented on any appliance capable of capturing and analyzing data from a network. For example, the capture system  310  described above could be implemented on one or more of the servers or clients shown in  FIG. 1 . Additionally, a capture system may interface with a network in any number of ways including, but not limited to, wirelessly. 
     Document Registration 
     The capture system described above implements a document registration scheme. A user registers a document with a capture system, the system then alerts the user if all or part of the content in the registered document is attempting to, or leaving, the network. Thus, unauthorized documents of various formats (e.g., Microsoft Word, Excel, PowerPoint, source code of any kind, and text are prevented) are prevented from leaving an enterprise. There are great benefits to any enterprise that keeps its intellectual property, and other critical, confidential, or otherwise private and proprietary content from being mishandled. Sensitive documents are typically registered with the capture system  200 , although registration may be implemented using a separate device. 
       FIG. 6  illustrates an embodiment of a capture/registration system. The capture/registration system  600  has components which are used in a similar or identical way to those of the capture system  300  shown in  FIG. 3 , including the network interface module  602 , the object store module  606 , user interface  612 , and object capture modules  604  (the packet capture  302 , object assembly  304 , and object classification  306  modules of  FIG. 3 ). 
     The capture/registration system  600  includes a registration module  610  interacting with a signature storage  608  (such as a database) to help facilitate a registration scheme. There are numerous ways to register documents. For example, a document may be electronically mailed (e-mailed), uploaded to the registration system  600  (for example through the network interface module  702  or through removable media), the registration system  600  scanning a file server (registration server) for documents to be registered, etc. The registration process may be integrated with an enterprise&#39;s document management systems. Document registration may also be automated and transparent based on registration rules, such as “register all documents,” “register all documents by specific author or IP address,” etc. 
     After being received, classified, etc., a document to be registered is passed to the registration module  610 . The registration module  610  calculates a signature or a set of signatures of the document. A signature associated with a document may be calculated in various ways. An exemplary signature consists of hashes over various portions of the document, such as selected or all pages, paragraphs, tables and sentences. Other possible signatures include, but are not limited to, hashes over embedded content, indices, headers, footers, formatting information, or font utilization. A signature may also include computations and meta-data other than hashes, such as word Relative Frequency Methods (RFM)—Statistical, Karp-Rabin Greedy-String-Tiling-Transposition, vector space models, diagrammatic structure analysis, etc. 
     The signature or set of signatures associated on a document is stored in the signature storage  608 . The signature storage  608  may be implemented as a database or other appropriate data structure as described earlier. In an embodiment, the signature storage  608  is external to the capture system  600 . 
     Registered documents are stored as objects in the object store module  606  according to the rules set for the system. In an embodiment, only documents are stored in the content store  606  of the object system network. These documents have no associated tag since many tag fields do not apply to registered documents. 
     As set forth above, the object capture modules  602  extract objects leaving the network and store various objects based on capture rules. In an embodiment, all extracted objects (whether subject to a capture rule or not) are also passed to the registration module for a determination whether each object is, or includes part of, a registered document. 
     The registration module  610  calculates the set of one or more signatures of an object received from the object capture modules  604  in the same manner as the calculation of the set of one or more signatures of a document received from the user interface  612  to be registered. This set of signatures is then compared against all signatures in the signature database  608 . However, parts of the signature database may be excluded from a search to decrease the amount comparisons to be performed. 
     A possible unauthorized transmission is detectable if any one or more signatures in the set of signatures of an extracted object matches one or more signatures in the signature database  608  associated with a registered document. Detection tolerances are usually configurable. For example, the system may be configured so that at least two signatures must match before a document is deemed unauthorized. Additionally, special rules may be implemented that make a transmission authorized (for example, if the source address is authorized to transmit any documents off the network). 
     An embodiment of a registration module is illustrated in  FIG. 7 . As discussed above, a user may select a document to be registered. The registration engine  702  generates signatures for the document and forwards the document to content storage and the generated signatures to the signature database  608 . Generated signatures are associated with a document, for example, by including a pointer to the document or to some attribute to identify the document. 
     The registration engine calculates signatures for a captured object and forwards them to the search engine  710 . The search engine  710  queries the signature database  608  to compare the signatures of a captured object to the document signatures stored in the signature database  608 . Assuming for the purposes of illustration, that the captured object is a Word document that contains a pasted paragraph from registered PowerPoint document, at least one signature of the registered PowerPoint signatures will match a signature of the captured Word document. This type of event is referred to as the detection of an unauthorized transfer, a registered content transfer, or other similarly descriptive term. 
     When a registered content transfer is detected, the transmission may be halted or allowed with or without warning to the sender. In the event of a detected registered content transfer, the search engine  710  may activate the notification module  712 , which sends an alert to the registered document owner. The notification module  712  may send different alerts (including different user options) based on the user preference associated with the registration and the capabilities of the registration system. 
     An alert indicates that an attempt (successful or unsuccessful) to transfer a registered content off the network has been made. Additionally, an alert may provide information regarding the transfer, such as source IP, destination IP, any other information contained in the tag of the captured object, or some other derived information, such as the name of the person who transferred the document off the network. Alerts are provided to one or more users via e-mail, instant message (IM), page, etc. based on the registration parameters. For example, if the registration parameters dictate that an alert is only to be sent to the entity or user who requested registration of a document then no other entity or user will receive an alert. 
     If the delivery of a captured object is halted (the transfer is not completed), the user who registered the document may need to provide consent to allow the transfer to complete. Accordingly, an alert may contain some or all of the information described above and additionally contain a selection mechanism, such as one or two buttons—to allow the user to indicate whether the transfer of the captured object is eligible for completing. If the user elects to allow the transfer, (for example, because he is aware that someone is emailing a part of a registered document (such as a boss asking his secretary to send an email), the transfer is executed and the captured object is allowed to leave the network. 
     If the user disallows the transfer, the captured object is not allowed off of the network and delivery is permanently halted. Several halting techniques may be used such as having the registration system proxy the connection between the network and the outside, using a black hole technique (discarding the packets without notice if the transfer is disallowed), a poison technique (inserting additional packets onto the network to cause the sender&#39;s connection to fail), etc. 
       FIG. 8  illustrates an embodiment of the flow of the operation of a registration module. An object is captured at  802 . This object was sent from an internal network source and designated for delivery inside and/or outside of the network. 
     A signature or signatures are generated for this captured object at  804 . This signature or signatures are generated in a manner as described earlier. The signatures of the captured document are compared to the signatures of registered documents at  806 . For example, the search engine  710  queries the signature database which houses the signatures for registers documents and compares these registered document signatures to the signatures generated for the captured document. 
     If there are no matches at  808 , then the captured object is routed toward its destination at  822 . This routing is allowed to take place because the captured object has been deemed to not contain any material that has been registered with the system as warranting protection. If there is a match at  808 , further processing is needed. 
     In an embodiment, the delivery of the captured object is halted at  810 . Halting delivery prevents any questionable objects from leaving the network. Regardless if the delivery is halted or not, the registered document that has signatures that match the captured object&#39;s signatures is identified at  812 . Furthermore, the identity of the user or entity that registered the document is ascertained at  814 . 
     The user or entity of the matching registered document is alerted to this attempt to transmit registered material at  816 . This alert may be sent to the registered user or entity in real-time, be a part of a log to be checked, or be sent to the registered user or entity at a later point in time. In an embodiment, an alert is sent to the party attempting to transmit the captured object that the captured object contains registered information. 
     A request to allow delivery of the captured object may be made to the registered user or entity at  818 . As described earlier, there are situations in which a captured object that contains registered material should be allowed to be delivered. If the permission is granted at  820 , the captured object is routed toward its destination at  822 . If permission is not granted, the captured object is not allowed to leave the network. 
     Signature Generation 
     There are various methods and processes by which the signatures are generated, for example, in the registration engine  702  in  FIG. 7 . 
     One embodiment of a flow to generate signatures is illustrated in  FIG. 9 . The content of a document (register or intercepted) is extracted and/or decoded depending on the type of content contained in the document at  910 . The content is extracted by removing the “encapsulation” of the document. For example, if the document is a Microsoft Word file, then the textual content of the file is extracted and the specific MS Word formatting is removed. If the document is a PDF file, the content has to be additionally decoded, as the PDF format utilizes a content encoding scheme. 
     To perform the text extraction/decoding at  910 , the content type of the document is detected (for example, from the tag associated with the document). Then, the proper extractor/decoder is selected based on the content type. An extractor and/or decoder used for each content type extracts and/or decodes the content of the document as required. Several off the shelf products are available, such as the PDF to Text software, may be used for this purpose. In one embodiment, a unique extractor and/or decoder is used for each possible content type. In another embodiment, a more generic extractor and/or decoder is utilized. 
     The text content resulting from the extraction/decoding is normalized at  920 . Normalization includes removing excess delimiters from the text. Delimiters are characters used to separate text, such as a space, a comma, a semicolon, a slash, tab, etc. For example, the extracted text version of an Microsoft Excel spreadsheet may have two slashes between all table entries and the normalized text may have only one slash between each table entry or it may have one space between each table entry and one space between the words and numbers of the text extracted from each entry. 
     Normalization may also include delimiting items in an intelligent manner. For example, while credit card numbers generally have spaces between them they are a single item. Similarly, e-mail addresses that look like several words are a single item in the normalized text content. Strings and text identified as irrelevant can be discarded as part of the normalization procedure. 
     In one embodiment, such evaluations are made by comparison to a pattern. For example, a pattern for a social security number may be XXX-XX-XXXX, XXXXXXXX, or XXX XX XXXX, where each X is a digit from 0-9. An exemplary pattern for an email address is word@word.three-letter-word. Similarly, irrelevant (non-unique) stings, such as copyright notices, can have associated patterns. 
     The pattern comparison is prioritized in one embodiment. For example, if an email address is considered more restrictive than a proper name and a particular string could be either an email address or a proper name, the string is first tested as a possible email address. A string matching the email pattern is classified as an email address and normalized as such. If, however, it is determined that the string is not an email address, then the string is tested against the proper name pattern (for example, a combination of known names). If this produces a match, then the string is normalized as a proper name. Otherwise the string is normalized as any other normal word. 
     By comparing the normalization patterns against the string to be normalized in sequence, an implicit pattern hierarchy is established. In one embodiment, the hierarchy is organized such that the more restrictive, or unique, a pattern is, the higher its priority. In other words, the more restrictive the pattern, the earlier it is compared with the string. Any number of normalization patterns useable and the list of patterns may be configurable to account for the needs of a particular enterprise. 
     Normalization may also include discarding text that is irrelevant for signature generation purposes. For example, text that is known not to be unique to the document may be considered irrelevant. The copyright notice that begins a source code document, such as a C++ source file, is generally not relevant for signature generation, since every source code document of the enterprise has the identical textual notice and would be ignored. Irrelevant text is identified based on matching an enumerated list of known irrelevant text or by keeping count of certain text and thus identifying frequently reoccurring strings (such as strings occurring above a certain threshold rate) as non-unique and thus irrelevant. Other processes to identify irrelevant text include, but are not limited to, identification through pattern matching, identification by matching against a template, and heuristic methods requiring parsing of examples of other documents of the same type. 
     The delimitated text items of the normalized text content are tokenized, and, converted into a list of tokens at  930 . In one embodiment, tokenizing involves only listing the delimited items. In another embodiment, each item is converted to a token of fixed size. Text items may be hashed into a fixed or configurable hash site such as binary number (for example, an 8-bit token). An exemplary hash function that may be used for tokenizing is MD5. 
     The document signatures are generated from the list of tokens at  940 . An exemplary embodiment of a flow for changing tokens into document signatures is described with reference to  FIG. 10 . The first M tokens from a list of tokens generated from a document are selected at  1010 , where M is an appropriate positive integer value. For example, if M is 10, then the first ten tokens from a list are selected. 
     Of the selected M tokens, N special tokens are selected at  1020 , N also being an appropriate positive integer and is less than, or equal to, M. The N special tokens may be selected at random, in part based on size, and/or in part on obscurity. Tokens that occur less frequently are more obscure and thus more likely to be selected as a special token. A token dictionary may be provided to log the frequency of tokens. 
     The special tokens may also be selected based on the type of the token as defined by the normalization pattern matched by the source string. As set forth above, during the normalization process, some strings are identified as higher priority text (such as email addresses, credit card numbers, etc.) the tokenization of which results in higher priority tokens. Thus, the selection of the N special tokens may take the source string into account. 
     Tokens may also have an associated priority value that may be used in selecting the special tokens. The priority value can be based on the priority of the normalization pattern matched by the token (for example, social security number, credit card number, email address, etc.) or based on additional signs of uniqueness, such as the frequency of capitalized letters, and the inclusion of special rare characters (for example, “̂”, “*”, “@”, etc.) 
     A hash signature of the N special tokens is calculated, resulting in one of the document signatures at  1020 . The hash is calculable in a number or ways. Special tokens may be hashed individually, or in groups, and the resultant hashes concatenated to form a signature, concatenated prior to the calculation, or hashed without concatenation at all. Any appropriate hash function and/or any combination of these hashing techniques may be utilized. 
     In one embodiment, before the next M tokens are selected, P tokens of the list of tokens are skipped from the first token of the M tokens. However, if P is zero, the next M tokens would be identical to the current M tokens, and therefore zero is not an allowed value for P. If P is less than M, then the next set of M tokens will overlap with the current set of M tokens. If P is equal to M, then the first token of the next M tokens will immediately follow the last token of the current M tokens. If P is greater than M, then some tokens are skipped between the next and the current M tokens. 
     A determination is made as to whether all signatures have been generated at  1040 . This is be done by observing if there are less than M tokens remaining on the list, hence, the next M tokens cannot be selected. If all signatures for the document have been generated, then the process terminates. However, if more signatures are to be generated for the document the next M tokens are selected by reverting to selecting tokens at  1010 . 
     There are numerous other ways to perform each of the proceedings of  FIGS. 9 and 10 . Some blocks are skipped entirely in some embodiments. For example, block  930  in  FIG. 9  may be skipped and the signatures generated directly from the normalized text. Regarding  FIG. 10 , various values may be used for M, N, and P, with each combination generating a different number of signatures. The specific configuration of M, N, and P thus depends on the needs of the enterprise and the volume and content of captured and registered documents. In an embodiment, M ranges between 8-20, N between 8-10, and P between 4-40. 
     An embodiment, of a registration engine that generates signatures for documents is illustrated in  FIG. 11 . The registration engine  1100  accepts documents, and generates signatures over these documents. The document may be one registered via the user interface, or one captured by the capture modules, as described earlier. 
     The registration engine  1100  includes an extractor/decoder  1102  to perform the functionality described with reference to block  910  of  FIG. 9 . The registration engine also includes a normalizer  1104  to perform the functionality described with reference to block  920  of  FIG. 9 . A tokenizer  1106  performs the functionality described with reference to  930  of  FIG. 9 . A signature generator  1108  performs the functionality described with reference to block  940  of  FIG. 9 . The signature  1100  generator may implement the process described with reference to  FIG. 10 . 
     Indexing 
     Searching for information about captured objects stored on a disk (either local or networked) is generally slow as each object must first be retrieved from the disk and then examined against the search criteria. As described below, by creating one or more fast storage (such as Random Access Memory, flash, processor cache, etc.) indexes containing information (such as metadata information and/or keywords) about the objects (and therefore the content) stored on a disk, the task of searching for information regarding captured objects is performed quicker. 
       FIG. 12  illustrates an exemplary embodiment of a network capture device utilizing indexing. The indexing network capture device  1212  includes a network interface module  1200 , packet capture module  1202 , object assembly module  1204 , object classification module  1206 , and an object store module  1208 . These modules operate in a manner consistent with those modules described earlier (for example, in  FIG. 3 ). During typical operation, the indexing network capture device  1212  captures and analyzes packet streams as described earlier. 
     The indexing network capture device  1212  also includes a capture indexer  1214  to create entries into word indexes  1216  consisting of a dictionary (or lists) of keywords found in all captured content (flows, documents, etc.) and/or entries into metadata indexes (or lists)  1218  based on captured content. In an embodiment, the capture indexer  1214  is a part of the object classification module  1206 . Keyword entries may point to a data structure containing the objects containing the keyword and/or point to a list of objects containing the keyword. A keyword is a word, phrase, name, or other alphanumeric term that exists within common textual content such as an email, Microsoft Office document, or similar content. Typically, only currently used indexes are stored in cache or RAM on the capture device, however, one or more of these indexes may also be stored on disk either locally or remotely. The persistence of these indexes to disk may be done on command or periodically. However, searching is faster if more indexes that are in RAM or other fast storage device rather than on disk. 
     A metadata index is a tree structure for an individual property (such as IP address) and a subsequent list of captured objects in capture storage device that have said property (such as “transmitted from the specific IP addresses”). Metadata includes properties describing the network characteristics of the content containing keywords. Examples of network characteristics include, but are not limited to, the source and destination addresses (Internet Protocol (IP) addresses), time and date of the transmission, size and name of the content, and protocol used to transmit the content. Additional descriptive properties may be used to describe the device upon which the content was captured, the user, viewer of the captured content or security settings of the captured content, etc. Much of this information is also found in tags as described earlier. 
     While the keyword index(es)  1216  and metadata index(es)  1218  are illustrated as a being separate entities, they may be a part of a single file per time period. 
     Because of the two index system, textual and numeric properties may be indexed using different indexing algorithms (for example, a keyword index may be a hash list and a metadata index a B-tree, etc.). Furthermore, metadata indexes that represent properties that may be enumerated (that have a limited number of possible values) may use different algorithms than those with unbounded properties. An example of an enumerated property is “protocol,” as there are a limited and known number of protocols that are supported by a network capture device. An example of an unbounded property is “size,” as an infinite number of possible sizes exist for the content that will be captured by a network capture device. 
     The capture indexer utilizes adaptive time-based dictionary granularity and creates new indexes over time, and therefore should prevent any specific index from growing unbounded. Accordingly, a specific maximum search time to find an arbitrary element in a tree or hash list is maintained. The temporal basis for creating a new index is determined by a plurality of factors including, but not limited to: a) the number of keywords or metadata elements that have been inserted into the index; b) the number of captured objects listed in the index; c) the aggregate size of the index; and d) the aggregate size of captured content being indexed. In an embodiment, the creation of new indices is additionally controlled by a user or administrator employing different heuristics to optimize search performance. 
     A search engine  1220  searches the indexes and returns a list of captured documents from capture storage device  1208  that match a specified search criteria. This search (or query) searches for each criteria component individually to retrieve a list of tags associated with objects in capture storage device  1208  for each criteria and then selects only those tags associated with objects that exist within all returned lists. Alternatively, selections may be made based on a captured object not existing within a returned list. An example of such a selection is the evaluation of the criteria “contains keyword confidential but not keyword sample.” In this case, only objects that exist within the first returned list (contains “confidential”) but not within the second returned list (contains “sample”) would be qualified as a result of the search. 
     While search engine  1220  is illustrated as a component inside of the capture device  1212 , it may exist on an external system. Additionally, the search engine  1220  may also have capabilities similar to those of the earlier described search engine. Similarly, a capture/registration system, as described before, may also utilize a capture indexer, indexes, and search engine. 
       FIG. 13  illustrates an exemplary indexing and searching flow. At  1301 , a packet stream is captured. This packet stream is analyzed at  1303  and a copy of the object and/or object data is moved to a storage device at  1305 . The capturing and analyzing of packet streams and moving objects and/or object data has been previously described. 
     Keyword index entries for the captured content are created at  1309 . This entry creation is performed by the capture indexer or equivalent. A keyword index may also be created, as necessary, at this point. 
     Metadata index entries for the captured content are created at  1311 . This entry creation is performed by the capture indexer or equivalent. A metadata index may also be created, as necessary, at this point. 
     Finally, one or more of the indexes (metadata or keyword) is queried to find a particular object in storage at  1313 . By querying the indexes instead of the objects themselves search time is greatly improved. If a match is found, the object, objects, and/or tag information may be retrieved from storage as desired. 
       FIG. 14  illustrates an example of a keyword and metadata index at a particular point in time. Each entry in the keyword index  1216  data structure includes both a keyword found in a document and a reference to that document. For example, the keyword index  1216  data structure includes keywords “confidential” and “information.” The keyword “confidential” was found by the capture system to be in documents “1” and “2.” Accordingly, the keyword index  1216  includes references to those documents for “confidential.” Similarly, each entry in the metadata index  1218  data structure includes both metadata data associated with a document and a reference to that document. For example, the metadata index  1218  data structure includes metadata “mailfrom Leopold” (indicating that an email originated from someone named “Leopold” contained a specific document), “health care information (HCI)” (indicating that a document included, generically, HCI), and “PDF” (indicating that a document was a PDF file). 
     The use of both a keyword index  1216  and metadata index  1218  allows for queries not possible with either a traditional keyword or metadata query. For example, by creating new index periodically (thereby having multiple indexes), a query of documents by time in addition to content is possible. In contrast, while a normal Internet search engine may be able to determine if a particular website has a particular keyword, that same search engine cannot determine if it had that same keyword 15 minutes ago, 1 week ago, etc. as these search engines employ one large index that does not account for time. 
     Additionally, previously there were no queries that could sort through both keyword and metadata. For example, a search for an email from a person named “Leopold,” that contains a PDF attachment, HCI, and includes (either in the PDF or in the body of the email) the words “confidential” and “information” was impossible. Database queries only search for metadata stored in indexed columns (e.g., such as if the content is a PDF file, mail from information, etc.). These queries do not account for keywords, in other words, they cannot search for a particular document containing the words “confidential” and “information.” Keyword queries (such as a Google query) cannot search for metadata such as the metadata described above. 
       FIG. 15  illustrates a simplified exemplary querying flow using metadata and keyword indexing. At  1501 , one or more keyword indexes are queried for one or more keywords. For example, in the query described above for the entries of  FIG. 14 , keyword indexes  1216  are queried for both “confidential” and “information.” The result of this query is that “confidential” and “information” are only collectively found in reference  1 . Essentially, the result of the query is the intersection of a query for “confidential” and a query for “information.” Of course any Boolean operator such as OR, NOT, etc. may be used instead of or in conjunction with the Boolean AND. Also, natural language based queries may be supported. 
     The metadata indexes  1218  are similarly queried at  1503 . For example, in the email query described above for the entries of  FIG. 14 , keyword indexes  1218  are queried for “HCI,” “mailfrom Leopold,” and “PDF.” The result of this query is that this set of metadata is only collectively found in reference  1 . 
     Because this search was not bound by a time frame, all available keyword and metadata indexes would be queried for these keywords. However, the number of keyword indexes queried is reduced for a time frame limited search. 
     At  1505 , the results of the previous queries are intersected to create a set of references that satisfy the overall query. In the example above, the result of this intersection would be reference  1 . Accordingly, only reference  1  would satisfy the collective query as it is the only reference to have all of the required criteria. 
     At  1507 , the file information associated with the references from the intersection of  1505  is retrieved. Typically, as described earlier, this information is stored as a tag in a tag database and is retrieved from there. However, the actual documents associated with the references may be retrieved. 
     While this simplified query flow queries a keyword index prior to a metadata index query the reverse order may be performed. Additionally, many other variations on the simplified flow are possible. For example, while not as efficient, a query flow that performs an intersection after each index query (or after two, three, etc. queries) may be utilized. Another example is performing a query for a first specific time period (querying a first particular set of one keyword and one metadata index that were created/updated during the same time period), intersecting the results of the first query, performing a query on a second specific time period (querying a second particular set of one keyword and one metadata index that were created/updated during the same time period), intersecting the results of first query with the results of the second query, etc. Yet another example is performing a query for a first specific time period (querying a first particular set of one keyword and one metadata index that were created/updated during the same time period), intersecting the results of the first query, performing a query on a second specific time period (querying a second particular set of one keyword and one metadata index that were created/updated during the same time period), intersecting the results of the second query, etc. and when all (or some pre-determined number of) queries have been performed and intersections calculated for each specific time period, intersecting all of the specific period intersection results. 
     An optimization for the above described system uses adaptive cache alignment. Adaptive cache alignment means that the capture indexer (or some other entity including a user) aligns memory and/or disk data structures of the indexes (or index entries) to be the size of the system&#39;s processor&#39;s cache lines (for example, Level 2 (L2) memory cache within the system&#39;s processor—this processor has not been illustrated in this application in order to not unnecessarily clutter the figures). If the processor&#39;s capabilities are unknown, upon initialization the capture device&#39;s processor is examined and a determination of the appropriate cache alignment is made based upon that examination. Of course, the cache alignment may also be pre-determined if the exact system specifications are known. In another embodiment, the capture indexer (or other entity) examines the block size of the file system (of the fundamental storage unit) and uses this size as part of the cache alignment. Additionally, memory (such as RAM, cache, etc.) used by the capture indexer may be pre-allocated to remove the overhead of allocating memory during operation. Furthermore, algorithms operating on the memory are tolerant of uninitialized values being present upon first use. This allows for the usage of the memory without the latency associated with clearing or resetting the memory to a known state or value. 
     Closing Comments 
     An article of manufacture may be used to store program code. An article of manufacture that stores program code may be embodied as, but is not limited to, one or more memories (e.g., one or more flash memories, random access memories (static, dynamic or other)), optical disks, CD-ROMs, DVD ROMs, EPROMs, EEPROMs, magnetic or optical cards or other type of machine-readable media suitable for storing electronic instructions. Program code may also be downloaded from a remote computer (e.g., a server) to a requesting computer (e.g., a client) by way of data signals embodied in a propagation medium (e.g., via a communication link (e.g., a network connection)). 
     In one embodiment, a capture system is an appliance constructed using commonly available computing equipment and storage systems capable of supporting the software requirements. 
       FIG. 16  shows an embodiment of a computing system (e.g., a computer). The exemplary computing system of  FIG. 16  includes: 1) one or more processors  1601 ; 2) a memory control hub (MCH)  1602 ; 3) a system memory  1603  (of which different types exist such as DDR RAM, EDO RAM, etc,); 4) a cache  1604 ; 5) an I/O control hub (ICH)  1605 ; 6) a graphics processor  1606 ; 7) a display/screen  1607  (of which different types exist such as Cathode Ray Tube (CRT), Thin Film Transistor (TFT), Liquid Crystal Display (LCD), Digital Light Processing (DLP), Organic LED (OLED), etc.; and 8) one or more I/O and storage devices  1608 . 
     The one or more processors  1601  execute instructions in order to perform whatever software routines the computing system implements. The instructions frequently involve some sort of operation performed upon data. Both data and instructions are stored in system memory  1603  and cache  1604 . Cache  1604  is typically designed to have shorter latency times than system memory  1603 . For example, cache  1604  might be integrated onto the same silicon chip(s) as the processor(s) and/or constructed with faster SRAM cells whilst system memory  1603  might be constructed with slower DRAM cells. By tending to store more frequently used instructions and data in the cache  1604  as opposed to the system memory  1603 , the overall performance efficiency of the computing system improves. 
     System memory  1603  is deliberately made available to other components within the computing system. For example, the data received from various interfaces to the computing system (e.g., keyboard and mouse, printer port, LAN port, modem port, etc.) or retrieved from an internal storage element of the computing system (e.g., hard disk drive) are often temporarily queued into system memory  1603  prior to their being operated upon by the one or more processor(s)  1601  in the implementation of a software program. Similarly, data that a software program determines should be sent from the computing system to an outside entity through one of the computing system interfaces, or stored into an internal storage element, is often temporarily queued in system memory  1603  prior to its being transmitted or stored. 
     The ICH  1605  is responsible for ensuring that such data is properly passed between the system memory  1603  and its appropriate corresponding computing system interface (and internal storage device if the computing system is so designed). The MCH  1602  is responsible for managing the various contending requests for system memory  1603  access amongst the processor(s)  1601 , interfaces and internal storage elements that may proximately arise in time with respect to one another. 
     One or more I/O devices  1608  are also implemented in a typical computing system. I/O devices generally are responsible for transferring data to and/or from the computing system (e.g., a networking adapter); or, for large scale non-volatile storage within the computing system (e.g., hard disk drive). ICH  1605  has bi-directional point-to-point links between itself and the observed I/O devices  1608 . A capture program, classification program, a database, a filestore, an analysis engine and/or a graphical user interface may be stored in a storage device or devices  1608  or in memory  1603 . 
     In the foregoing specification, the invention has been described with reference to specific exemplary embodiments thereof. It will, however, be evident that various modifications and changes may be made thereto without departing from the broader spirit and scope of the invention as set forth in the appended claims. The specification and drawings are, accordingly, to be regarded in an illustrative rather than a restrictive sense. 
     Thus, a capture system and a document/content registration system have been described. In the forgoing description, various specific values were given names, such as “objects,” and various specific modules, such as the “registration module” and “signature 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, may be implemented as software or hardware modules, combined 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.