Abstract:
Described are computer-based methods and apparatuses, including computer program products, for detecting fraudulent activity. A computing device in a second network receives a plurality of interactions between data centers in a first network and one or more devices, remote from the data centers, of a user, the interactions forming an aggregate user session representing the user&#39;s activity over a period of time with the data centers via the devices. The computing device monitors data transaction requests of the interactions and is configured to respond to each request with a response that appears as if the request was executed. If the interactions are harmful, the computing device interdicts the interactions from execution by the data centers and allows the devices to submit additional interactions. If unable to determine whether the subsequent interactions are harmful, the computing device transmits the interactions to the data centers for execution and marks the interactions.

Description:
CROSS REFERENCE TO RELATED APPLICATIONS 
       [0001]    This application is a continuation of U.S. patent application Ser. No. 11/559,784 filed on Nov. 14, 2006, the disclosure of which is hereby incorporated by reference in its entirety. 
     
    
     FIELD OF THE INVENTION 
       [0002]    The present invention relates generally to computer-based methods and apparatuses, including computer program products, for detecting fraudulent activity. 
       BACKGROUND 
       [0003]    The increased use of networks to access and provide information has caused a dramatic increase in the amount of data transmitted over networks. To handle this increased amount of traffic, computer systems used to store, process, and transmit the information have been increasing in size and distributed among data centers. This distribution among data centers allows for increased speed which allows for decreased response time in the retrieval and processing of information. 
         [0004]    However, the increased speed and diversified data centers create an issue with the data that is sent to a plurality of data centers. The issue is that the data sent to and from users is distributed among the data centers. When the data from only one data center is analyzed, then it is extremely challenging if not impossible to obtain a complete representation of a user&#39;s activity. 
         [0005]    The ability to analyze a user&#39;s activity as a whole is important in a wide spectrum of industries that provide trusted services over a network. These industries have to be able to analyze user activity to provide feedback and improve performance of the systems. In addition, industries have to be able to effectively and efficiently identify fraudulent activity on their networks. 
         [0006]    Fraudulent activity has been increasing along with the rise in network based activity. Industries have responded by utilizing fraud detection systems to attempt to stop the loss of money and prestige. However, it has been challenging if not impossible for these fraud detection systems to collect the data from all of the data centers in real time and without impacting the customer&#39;s experience. Since fraudulent activity is increasing, it is important for industries, such as the financial services industry, to have a fraud detection system that can collect data packets from a plurality of data centers and reconstruct the data for the detection of fraudulent activity and other uses. 
       SUMMARY OF THE INVENTION 
       [0007]    The invention, in one aspect, features a method for detecting fraudulent activity. A computing device in a second network receives a plurality of interactions between a plurality of data centers in a first network and one or more devices, remote from the data centers, of a user, the plurality of interactions forming an aggregate user session representing the user&#39;s activity over a period of time with the plurality of data centers via the one or more devices. The computing device monitors data transaction requests of the plurality of interactions, the computing device configured to respond to each data transaction request with a response that appears to the user as if the request was executed by the respective data center. If the computing device determines that the interactions are harmful, the computing device interdicts the interactions from execution by the plurality of data centers and allows the one or more devices to submit additional interactions. If the computing device is unable to determine whether the subsequent interactions are harmful, the computing device transmits the interactions to the plurality of data centers for execution and marks the interactions for monitoring purposes. 
         [0008]    The invention, in another aspect, features a system for detecting fraudulent activity. The system includes a computing device in a second network configured to receive a plurality of interactions between a plurality of data centers in a first network and one or more devices, remote from the data centers, of a user, the plurality of interactions forming an aggregate user session representing the user&#39;s activity over a period of time with the plurality of data centers via the one or more devices. The computing device is configured to monitor data transaction requests of the plurality of interactions, the computing device configured to respond to each data transaction request with a response that appears to the user as if the request was executed by the respective data center. If the computing device determines that the interactions are harmful, the computing device is configured to interdict the interactions from execution by the plurality of data centers and allow the one or more devices to submit additional interactions. If the computing device is unable to determine whether the subsequent interactions are harmful, the computing device is configured to transmit the interactions to the plurality of data centers for execution and mark the interactions for monitoring purposes. 
         [0009]    The invention, in another aspect, features a computer program product, tangibly embodied in a non-transitory computer readable storage medium, for detecting fraudulent activity. The computer program product includes instructions operable to cause a computing device in a second network to receive a plurality of interactions between a plurality of data centers in a first network and one or more devices, remote from the data centers, of a user, the plurality of interactions forming an aggregate user session representing the user&#39;s activity over a period of time with the plurality of data centers via the one or more devices. The computer program product includes instructions operable to cause the computing device to monitor data transaction requests of the plurality of interactions, the computing device configured to respond to each data transaction request with a response that appears to the user as if the request was executed by the respective data center. If the computing device determines that the interactions are harmful, the computer program product includes instructions operable to cause the computing device to interdict the interactions from execution by the plurality of data centers and allow the one or more devices to submit additional interactions. If the computing device is unable to determine whether the subsequent interactions are harmful, the computer program product includes instructions operable to cause the computing device to transmit the interactions to the plurality of data centers for execution and mark the interactions for monitoring purposes. 
         [0010]    In some embodiments, any of the above aspects can include one or more of the following features. In some embodiments, if the computing device determines that the interactions are harmful and if the computing device is able to simulate the interactions, the computing device transmits a response for each interaction to the one or more devices, the transmitted responses appearing to the user as if the plurality of data centers executed the interactions. If the computing device determines that the interactions are harmful and if the computing device is unable to simulate the interactions, the computing device transmits a response for each interaction to the one or more devices, the transmitted responses appearing to the user as if the plurality of data centers failed to execute the interactions due to a system error. The computing device monitors data transaction requests of additional interactions submitted from the one or more devices. 
         [0011]    In some embodiments, the computing device generates a signature based on the monitoring step and correlates the signature with previous user sessions to determine whether the plurality of interactions and the previous user sessions share one or more characteristics. In some embodiments, the characteristics include at least one of: a common user, a common remote device, a common location, or a common activity pattern. In some embodiments, the signature is generated using one or more data analysis tools that have access to the plurality of interactions. 
         [0012]    In some embodiments, detecting fraudulent activity is performed in real time. In some embodiments, the response that appears to the user as if the request was executed by the respective data center includes dummy data to prevent the user from accessing data stored in the data center. In some embodiments, if the computing device determines that the interactions are harmful, the computing device provides responses to the additional interactions that encourage the user to stay connected to the computing device until an enforcement action is executed. In some embodiments, the responses to the additional interactions extend a session between the computing device and the one or more remote devices. 
         [0013]    In some embodiments, if the computing device determines that the interactions are harmful, the computing device determines one or more vulnerabilities associated with the plurality of data centers based on the interactions submitted by the one or more remote devices. 
         [0014]    Any of the aspects and examples above can provide one or more of the following advantages. They can reconstruct data and/or unify user sessions from multiple data centers. The reconstructed data and/or unified user sessions can be used for analysis as a complete or partial representation of a user&#39;s activity. The reconstruction of data packets into data allows for an improved representation of the user&#39;s activity to be analyzed, since data packets from only one data center most likely only shows the user&#39;s interactions with that data center and not the interactions with all of the data centers. A representation of the user&#39;s activity allows for a holistic analysis of the activity to detect fraudulent activity, to provide feedback to the user and of the system, and/or to improve performance of the system. 
         [0015]    Other advantages include the unification of user requests into user sessions for fraud detection. The unification of user requests that are transmitted to a plurality of data centers allows the fraud detection system to analyze an improved representation of the user&#39;s activity. This improved representation of the user&#39;s activity is more than the user&#39;s interactions with one data center in the plurality of data centers. It is part or all of the user&#39;s activity with the plurality of data centers. This allows the user&#39;s activity to be analyzed as a whole instead of piece by piece. 
         [0016]    Other advantages include the capture of data packets and/or user requests from the network without interfering with the transmission of the data packets and/or user requests to the rest of the network (e.g., the server system). The processing of the data packets and/or the user requests by the server systems is not interfered with by the capture of the data packets and/or the user requests. 
         [0017]    Other advantages include the processing of the data and/or user session by the fraud detection system in real time. The data collection system collects the data packets and/or user requests at the same time that the server systems receive the data packets and/or user requests. The reconstructed data and/or unified user sessions are processed by the fraud detection system at the same time that the server system processes the data and/or user request. The real time fraud detection allows for the interdiction of fraudulent activity before the fraudulent activity can harm the system (e.g., the fraud detection system can stop fraudulent transactions before the fraudulent transaction is completely processed). 
         [0018]    Other advantages include the memory module being volatile memory. The processing of data and/or user sessions to determine whether fraudulent activity exists occurs in real time. The fraud detection system utilizes the fast access capabilities of the volatile memory to access the elements stored in the memory module and analyze the data and/or user sessions using the stored elements in real time. 
         [0019]    Other advantages include the data bus subscription service. The data bus subscription service is the subscription of data feeds on the network. The subscription of data feeds on the network allows other requestor systems to access the data that is collected from the plurality of data centers. By providing the subscription service, the other requestor systems, such as a performance analysis system and a customer service system, can analysis the entire representation of the user&#39;s activity instead of just a portion of the user&#39;s activity that is transmitted through one data center out of the plurality of data centers. 
         [0020]    Other aspects and advantages of the present invention will become apparent from the following detailed description, taken in conjunction with the accompanying drawings, illustrating the principles of the invention by way of example only. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0021]    The foregoing and other objects, features, and advantages of the present invention, as well as the invention itself, will be more fully understood from the following description of various embodiments, when read together with the accompanying drawings. 
           [0022]      FIG. 1  is a functional block diagram of an exemplary system which reconstructs the data transmitted on the system and transmits the data to a fraud detection system. 
           [0023]      FIG. 2  is a functional block diagram of an exemplary system depicting the data being transported from the data centers to a data bus. 
           [0024]      FIG. 3  is a functional block diagram of an exemplary system showing the unification of user sessions from user requests. 
           [0025]      FIG. 4  is a functional block diagram of an exemplary system depicting parts of the fraud detection system including a database of user sessions. 
           [0026]      FIG. 5  is a functional block diagram of an exemplary system showing systems that receive data from the data bus. 
           [0027]      FIG. 6A  is a screen shot of a login module. 
           [0028]      FIG. 6B  is a diagram of information sent to a login module. 
           [0029]      FIG. 6C  is a screen shot of a search module. 
           [0030]      FIG. 6D  is a diagram of information transmitted to a search module. 
           [0031]      FIG. 6E  is a diagram of information received from a search module. 
           [0032]      FIG. 6F  is a screen shot of information received from a search module. 
           [0033]      FIG. 6G  is a diagram of information transmitted to a transaction module. 
           [0034]      FIG. 6H  is a diagram of information received from an information module. 
           [0035]      FIG. 6I  is a screen shot of information received from an information module. 
           [0036]      FIG. 7  is a flowchart depicting the transmission of data through an exemplary system. 
           [0037]      FIG. 8  is a flowchart depicting the transmission of user sessions through an exemplary system. 
           [0038]      FIG. 9  is a flowchart showing the processing of a user request being transmitted to a data center and through both the server system at the data center and the data collection system. 
           [0039]      FIG. 10  is a flowchart showing the processing of data packets through the system and transmission of the data packets to requestor systems. 
           [0040]      FIG. 11  is a flowchart showing the interdiction of user sessions when fraudulent activity is detected. 
           [0041]      FIG. 12  is a flowchart showing the communication of fraudulent activity to the authentication system. 
           [0042]      FIG. 13  is a flowchart showing the creation of rules in response to the detection of fraudulent activity. 
           [0043]      FIG. 14  is a flowchart showing the redirection of a user session to a separate network to mitigate fraudulent activity. 
       
    
    
     DETAILED DESCRIPTION 
       [0044]      FIG. 1  is a functional block diagram of an exemplary system  100  which reconstructs the data transmitted on the system and transmits the data to a fraud detection system  160 . In some examples, the data includes records of interactions between the user  112  and the system  100 . The records of interactions include, for example, buy and sell orders from the user  112  to a brokerage company and/or credit card transactions of the purchase of products and services by the user  112 . 
         [0045]    The transmission on the system  100  can occur over packet-based networks and/or circuit-based networks. Packet-based networks can include, for example, the Internet, a carrier internet protocol (IP) network (e.g., local area network (LAN), wide area network (WAN), campus area network (CAN), metropolitan area network (MAN), home area network (HAN)), a private IP network, an IP private branch exchange (IPBX), a wireless network (e.g., radio access network (RAN), 802.11 network, 802.16 network, general packet radio service (GPRS) network, HiperLAN), and/or other packet-based networks. Circuit-based networks can include, for example, the public switched telephone network (PSTN), a private branch exchange (PBX), a wireless network (e.g., RAN, Bluetooth, code-division multiple access (CDMA) network, time division multiple access (TDMA) network, global system for mobile communications (GSM) network), and/or other circuit-based networks. 
         [0046]    The system  100  includes the user  112  who interacts with a transmitting device  110  which transmits data in one or more parts as data packets to a load balancer  130 . In some examples, the user  112  uses any type of transmitting device  110  to transmit the data packets. The transmitting device  110  includes, for example, a computer, a computer with a browser device, a telephone, an IP phone, a mobile device (e.g., cellular phone, personal digital assistant (PDA) device, laptop computer, electronic mail device), and/or other communication devices. The browser device includes, for example, a computer (e.g., desktop computer, laptop computer) with a world wide web browser (e.g., Microsoft® Internet Explorer®, Mozilla® Firefox). The mobile computing device includes, for example, a Blackberry®. 
         [0047]    The load balancer  130  can be a network system. The network system includes, for example, a network router, a network switch, a network hub, a computer and/or other communication devices. The network system includes, for example, one or more network adapters (e.g., 10/100/1000 Base-T network interface adapter card (NIC), 1000 Base-SX NIC, 1000 Base-LX NIC, 1000 Base-FX NIC). The network system includes one or more modules that process the data packets. The module is implemented in digital electronic circuitry, computer hardware, firmware, and/or software. The module includes, for example, a data routing module, an internet protocol (IP) routing module, a domain name service (DNS) routing module, and/or other routing modules. 
         [0048]    In some examples, the system  100  receives data packets from other systems. The other systems include one or more network systems that each contains one or more transmitting devices  110 . The other systems transmit data to the load balancer  130  for transmission to the data centers  120   a  and  120   b  for processing. 
         [0049]    The load balancer  130  transmits the data packets to the different data centers  120   a  or  120   b  based on load balancing techniques. In some examples, the load balancer  130  transmits the data packets to a data center selected from the plurality of data centers  120   a  and  120   b  according to the available capabilities of the data centers (e.g., processor availability, disk capacity), the conditions of the network (e.g., packet trip time, packet losses), a quality of service indicators on the data packet, application availability, number of connections to each data center, and/or a pre-defined routing instruction. 
         [0050]      FIG. 1  shows an exemplary system  100  with data centers A  120   a  and B  120   b . Although the system  100  is shown with two data centers  120   a  and  120   b , other examples include any number of data centers (e.g., three, four, ten, twenty, one hundred, one thousand, ten thousand). Similarly, although the system  100  illustrates one load balancer  130 , other examples contain a plurality of load balancers in multiple layers. For example, a user&#39;s transmitting device  110  transmits data packets to the first load balancer  130  which then has a layer of load balancers to which the first load balancer  130  transmits the data packet. The second load balancer on the layer below the first load balancer  130  transmits the data packet to a data center selected from the plurality of data centers. 
         [0051]    The data packets in the data centers  120   a  and  120   b  are transmitted to the server systems  122   a  and  122   b  and the data collection systems  124   a  and  124   b , respectively. For example, a data packet is transmitted from the user&#39;s transmitting device  110  to the load balancer  130 . The data packet is transmitted to data center A  120   a  from the load balancer  130 . The data packet is transmitted in the data center A  120   a  to the server system  122   a  and to the data collection system  124   a.    
         [0052]    The transmission of the data packet in the data center A  120   a  to the server system  122   a  and to the data collection system  124   a  can occur simultaneously and independently from each other using for example, a network device. The network device includes, for example, a network router, a network firewall, a network hub, a network switch, a computer, and/or other network devices (e.g., Gigamon GigaVUE-MP available from Gigamon Systems LLC). 
         [0053]    The server systems  122   a  and  122   b  each can include one or more servers (e.g.,  126   a ,  127   a ,  127   b , and  128   b ). The server includes, for example, a web server (e.g.,  126   a ), an application server (e.g.,  128   b ) (e.g., Oracle® Application Server 10g available from Oracle Corporation), a database server (e.g.,  127   a  and  127   b ) (e.g., Oracle® Database 10g available from Oracle Corporation), a communication server, a fax server, a file server, a game server, an authentication server (e.g., RSA® Authentication Manager available from RSA Security Inc.), a desktop computer, a central ad server, a file transport protocol server, an image server, a mail server, a news server, a proxy server, a printer server, a sound server, a streaming media server, a terminal server, a firewall, a network router, a network hub, a network (e.g., an intranet  125   a ) and/or a network switch. 
         [0054]    In some examples, the data collection system  124   a  and  124   b  includes one or more computing devices which can be, for example, a computer, a laptop computer, a network router, a network switch, and/or a network hub (e.g., Radware® AS4 available from Radware Ltd., Covelight Inflight™ 5000 available from Covelight System, Inc., Gigamon GigaVUE-MP available from Gigamon Systems LLC). 
         [0055]    The data collection systems  124   a  and  124   b  can capture the data packets from the network without interfering with the transmission of the data to the rest of the network. For example, the data collection system  124   a  receives data packets transmitted to the data center  120   a  from the load balancer  130  while at the same time the data packets are received by the server system  122   a , creating a parallel path for receiving the data packets. 
         [0056]    The data collection systems  124   a  and  124   b  can capture the data packets transmitted by the server systems  122   a  and  122   b , respectively, back to the user. For example, the user  112  using a transmitting device  110  transmits a data packet requesting information. The data packet is routed through the load balancer  130  to data center B  120   b . The data packet is transmitted to the server system  122   b  and the data collection system  124   b . The server system  122   b  processes the data packet and responds to the user&#39;s request by transmitting a data packet response. The data packet response is captured by the data collection system  124   b.    
         [0057]    The data packet and/or data packet response are transmitted from the data collection systems  124   a  and  124   b  to the reconstruction engine  150 . The reconstruction engine  150  receives data packets from the plurality of data centers  120   a  and  120   b . The reconstruction engine  150  processes the data packets to form part or all of the data from the user  112 . The reconstructed data is a representation of part or all of the information transmitted over the network by the user&#39;s transmitting device  110  (e.g., the raw information that is transmitted over the network). 
         [0058]    The processing of the data packets by the server system  122   a  can occur at or near the same time and separately from the reconstruction engine  150  reconstructing the data. For example, the data packets are transmitted through the server system  122   a  for processing at or near the same time as the data collection system  124   a  transmits the data packets to the reconstruction engine  150  and the reconstruction engine  150  is reconstructing the data packets into data. The processing by the server system  122   a  includes, for example, the transmission of the data packets received by the server system  122   a  to an intranet  125   a  which transmits the data packet to a web server  126   a . The web server  126   a  processes the data packet to determine if the web server  126   a  needs to respond to the data packet and/or if the web server  126   a  needs to access information from the database server  127   a . The web server  126   a  needs information from the database server  127   a  to respond to the data packet (e.g., a search submission to a search module that is part of the web server  126   a ). The web server  126   a  queries the database server  127   a  and the database server  127   a  responds to the query (e.g., the information that is requested from the search). The web server  126   a  processes the information returned from the query and transmits a web page with the requested information back to the transmitting device  110  of the user  112  through the intranet  125   a.    
         [0059]    The reconstruction engine  150  can analyze the data packets to determine data packet information. The data packet information includes a destination parameter, and/or an origination parameter. The data packet parameters can be used by the reconstruction engine  150  to match data packets together to form the data of the user  112 . The data packet parameters includes a network address, a user address, an operating system (OS) fingerprint, a network card address, a user cookie, form data, an encryption key, and/or a transaction identifier. The network address includes the address of the transmitting device  110  transmitting the data packet, the address of the network address translation device (e.g., firewall) that transmits the data packet, or other network devices that transmit data packets. The user address includes the address of the user  112  at the transmitting device  110  transmitting the data packet, identifying information of the user&#39;s transmitting device  110 , or other identifying information that is associated with the user  112 . The OS fingerprint includes the formatting of the data that indicates the OS that transmits the data packet, identifying information in the data packet from the OS, or other identifying information that is associated with the OS. The network card address includes the address of the network card in the user&#39;s transmitting device  110  or other identifying information of the transmitting device that is associated with the transmitting device  110 . The user cookie includes information stored on the user&#39;s transmitting device  110  (e.g., information stored in a web browser on the user&#39;s transmitting device  110 ) or other identifying information that is stored on the user&#39;s transmitting device  110 . The form data includes information in the data packets associated with the user  112  (e.g., information in the data packet such as UserID=GeorgeSmith) or other identifying information in the data packets. 
         [0060]    In some examples, the reconstruction engine  150  processes the data packet to determine if the data packet matches a request from the server systems  122   a  and  122   b . For example, the server system  122   a  transmits a request for information (i.e., a data packet requesting information from a user  112 ) to a user  112  (e.g., login information—userid and password). The user  112  responds through the transmitting device  110  by transmitting a data packet (e.g., userid and password). The reconstruction engine  150  matches the request for information from the server system  122   a  to the data packet from the user  112  (e.g., the request for login information to the login information). The matching of the request for information from the server system  122   a  to the data packet from the user  112  is used by the reconstruction engine  150  to match data packets together. 
         [0061]    The reconstruction engine  150  can process the data packets. The processing of the data packets includes filtering, decrypting, and/or encrypting (e.g., Radware® SSL Decryption available from Radware Ltd.). The filtering includes, for example, filtering by an internet protocol address, a protocol, a data type (e.g., graphic interface format (gifs), hypertext markup language (html), joint photographic experts group (jpegs), cascading style sheets (css), javascript (js)), a parameter (e.g., password, pin, personal identifying information), content, a content-type, an uniform resource locator (URL) path, and/or an internet protocol (IP) range. 
         [0062]    The reconstruction engine  150  receives, for example, over one billion data packets per hour. For example, the reconstruction engine  150  can filter out data packets that are from a trusted internet protocol address (e.g., address 10.111.23.12 which is from a trusted data center). The reconstruction engine  150  filters out all of the data packets from the trusted internet protocol address to reduce the load on the reconstruction engine  150 . For example, the reconstruction engine  150  can filter out parameters, such as passwords and pin numbers, to protect sensitive information from being transmitted to the fraud detection system  160 . 
         [0063]    The reconstruction engine  150  transmits the data to the fraud detection system  160 . The fraud detection system  160  processes the data to detect fraudulent activity. The processing of the data includes, for example, one or more systems that process the data (e.g., TIBCO® BusinessEvents® available from TIBCO Software Inc., Deep Network Analyzer (DNA) available from SourceForge®). 
         [0064]      FIG. 2  is a functional block diagram of an exemplary system  200  depicting the data being transported from the data centers  120   a  and  120   b  to a data bus  210 . The system  200  includes the user  112  using the transmitting device  110  which transmits data in one or more parts as data packets to the load balancer  130 . The load balancer  130  transmits the data packets to a data center in the plurality of data centers  120   a  and  120   b  based on load balancing techniques. The data packets are transmitted to the server systems  122   a  and  122   b  and to the data collection systems  124   a  and  124   b , respectively. 
         [0065]    The data collection systems  124   a  and  124   b  transmit the data packets to the data bus  210 . In some examples, the data bus  210  aggregates the data packets from the plurality of data centers  120   a  and  120   b  for distribution to the systems that need and/or request the data such as the reconstruction engine  150 . 
         [0066]    The data packets are transmitted from the data bus  210  to a database  220  and to the reconstruction engine  150 . The reconstruction engine  150  reconstructs data from the data packets received from the plurality of data centers  120   a  and  120   b . The reconstructed data is transmitted to the fraud detection system  160  and the database  220 . 
         [0067]    In some examples, the data bus  210  includes one or more computer systems connected to the network (e.g., a computing blade, a network server, a network router, a network switch, a network hub). In some examples, the database  220  stores data packets and/or data. The database  220  includes, for example, a memory module. The memory module includes, for example, persistent storage and/or volatile storage. 
         [0068]      FIG. 3  is a functional block diagram of an exemplary system  300  showing the unification of user sessions from user requests. The system  300  includes the user  112  who has a user session  310 . The user session  310  is a compilation of all of the user requests associated with the user  112 . The user  112  is requesting and receiving information from the system  300 . This information requesting and receiving is accomplished through user requests. 
         [0069]    The user requests are transmitted to the load balancer  130 . The load balancer  130  transmits a user request to a data center  120   a  or  120   b  based on load balancing techniques. The user request is transmitted to the server system  122   a  or  122   b  and the data collection system  124   a  or  124   b  based on the data center (e.g.,  120   a  or  120   b ) to which the user request is sent. The data collection system  124   a  or  124   b  transmit the user request to a data bus  210 . The user request is transmitted from the data bus  210  to the database  220  and an unification engine  320 . The unification engine  320  combines all of the received user requests from the user  112  to form an unified user session which comprises all or part of the user session  310 , which was initially split apart for transmission to the data centers  120   a  and  120   b . The unified user session can, for example, include a representation of part or all of the user&#39;s activity including interactions that are not transmitted through the transmitting device  110  over the network (e.g., information associated with applications on the transmitting device such as an application that interfaces with the server systems  122   a  and  122   b ). 
         [0070]    The unification engine  320  can analyze the user requests to determine user request information. The user request information includes an origination parameter and/or a destination parameter. One or more of the user request parameters are, for example, used by the unification engine  320  to match user requests together to form the unified user session of the user  112 . 
         [0071]    The unification engine  320  can analyze the user requests to determine if the user request matches a request for information from the server systems  122   a  and  122   b  and/or a request for information from an application module associated with the user&#39;s transmitting device  110  (e.g., client side application that communicates with the server systems  122   a  and  122   b ). For example, the application module associated with the user&#39;s transmitting device  110  requests information from the user  112 . The user  112  inputs the information into the application module. The application module through the transmitting device  110  transmits the information in the form of an user request to the server system  122   a  or  122   b  for processing. The unification engine  320  matches the user request with information that is requested by the application module (i.e., the unification engine  320  is aware of what information is requested by application modules so that it can match the information to user requests). The matching of the request for information from the application module to the user request form the user  112  is used by the unification engine  320  to match user requests together to form user sessions. 
         [0072]    The unification engine  320  can process the user requests. The processing of the user requests includes, for example, filtering, decrypting, and/or encrypting. 
         [0073]    The user session unified by the unification engine  320  is transmitted to the database  220  for storage and to the fraud detection system  160 . If the fraud detection system  160  detects fraudulent activity, then the fraud detection system  160  communicates to the interdiction system  330 . The interdiction system  330 , for example, stops the communication of the user requests to the load balancer  130 . 
         [0074]      FIG. 4  is a functional block diagram of an exemplary system  400  depicting parts of a fraud detection system  405  including a database  450  of user sessions. Parts of the exemplary system  400  are described above in  FIGS. 1 ,  2 , and  3 . 
         [0075]    The unification engine  320  unifies the user requests and transmits the unified user session to the fraud detection system  405 . The fraud detection system  405  includes a geolocation profiling engine  410 , a behavior profiling engine  420 , an application rules engine  430 , a transactional rules engine  440 , and a database  450 . The fraud detection system  405  can, for example, include one of the engines or combinations of the engines. 
         [0076]    The geolocation profiling engine  410  analyzes the user location, the network path, and/or the network address. For example, the geolocation profiling engine  410  analyzes the network address of the transmitting device  110  to determine if the network address is one that is known as a suspicious address (e.g., a network address from a foreign country that is known to have a high amount of fraudulent activity). The determination that the network address is suspicious includes all or part of the information analyzed by the fraud detection system  405  to determine whether the activity is fraudulent. For example, the geolocation profiling engine  410  analyzes the network path of the user  112  to determine if the pathway that the user  112  is accessing travels through or is intercepted by a suspicious network. If the network path of the user  112  is being intercepted by a suspicious network, the geolocation profiling engine  410  reports the fraudulent activity to the fraud detection system  405 . 
         [0077]    The behavior profiling engine  420  analyzes behavior by analyzing how many page views without a logon occur, visits with malicious software, search terms that are categorized as suspect, uniform resource locators (URL) that are categorized as suspect, account access that is categorized as rapid access, access into multiple accounts, multiple network connections per session, and/or multiple users per session. 
         [0078]    For example, if the user  112  accesses ten different accounts which are not interconnected, then the behavior profiling engine  420  determines that the user activity is fraudulent and reports the fraudulent activity to the fraud detection system  405 . However, if the user  112  accesses ten different accounts but the accounts are all interconnected (e.g., all have the same mailing address, the same unique user identifier), then the behavior profiling engine  420  determines that the user activity is not fraudulent. 
         [0079]    The behavior profiling engine  420  includes an anomaly-based profiling engine. The anomaly-based profiling engine uses a baseline of normal activity to detect when activity falls outside of the baseline. The baseline of normal activity can, for example, be programmed into the profiling engine, and/or learned by the profiling engine while processing the information, data, and/or user sessions on the network. The activity includes any event, state, content, behavior, transaction, and/or similar processing by the user  112  with the network  400 . 
         [0080]    For example, the user  112  changes her email address on the exemplary system  400  from an internet service provider (e.g., standarduser at standardisp.com) that the user had been using for ten years to an email address at a generic easily obtained email hosting service (e.g., genericuser at hotmail.com). This change of the user&#39;s email address may be normal behavior for some users but for this particular user  112 , the change in email addresses falls outside of the normal behavior and the behavior profiling engine  420  classifies the activity as fraudulent activity. The classification of the activity as fraudulent activity requires, for example, the user  112  to confirm the change of email addresses outside of the communication channel in which the email change was submitted (e.g., if the email address change was through a website associated with the system  400 , then the email address change confirmation outside of the communication channel is a phone call from the system  400 ). 
         [0081]    The application rules engine  430  monitors the interactions between the modules of the exemplary system  400 . The application rules engine  430  monitors, for example, a login module, a search module, a transaction module, an information module, and/or other modules that are part of the exemplary system  400 . The application rules engine  430  monitors the modules of the exemplary system  400  for the detection of an interaction that is outside of the normal range of interactions with the module (e.g., violates a rule in the engine). The monitoring of the modules allows for the fraud detection system  405  to detect patterns of fraud that may not be detected by just analyzing the transaction of a user, but may be detected by analyzing the interactions with the applications of the exemplary system. 
         [0082]    For example, a set of users is attempting to exploit a security hole in the authentication module. The application rules engine  430  is monitoring the authentication module and detects that the set of users is attempting to authenticate to the system  400  using the same username and password (e.g., a buffer overflow in the username and/or password fields). The normal range of interactions is not for more than one user to attempt to authenticate to the system  400  using the same username and password. Accordingly, the application rules engine  430  determines that the activity of the set of users is fraudulent. 
         [0083]    The transactional rules engine  440  assigns a confidence level to the user session. The confidence level is an analysis to determine the level at which the system  400  ranks the user session. For example, if the user  112  is transmitting from a location that is within the user&#39;s profile (e.g., the user&#39;s home computer), but the transaction by the user  112  is outside of the user&#39;s profile (e.g., transferring all of the money in a money market account to an overseas bank account when the user never transferred money to an overseas bank account), then the confidence level of the user session is assigned a low number. This low number is utilized by the transactional rules engine  440  and/or the fraud detection system  405  to determine whether the activity is fraudulent. 
         [0084]    However, for example, if the user  112  is not coming from a location that is within the user&#39;s profile (e.g., a hotel in New York City when the user never logged in from New York City before), but the transaction by the user  112  is within the user&#39;s profile (e.g., transferring all of the money in a money market account to an overseas bank account when the user transfers money to an overseas bank account every other week) and the computer signature of the computer that the user is using matches the computer signature in the user&#39;s profile (e.g., the user is using her laptop normally used at home at the hotel in New York City), then the confidence level of the user session is a higher number since the transaction is within the user&#39;s normal behavior although the user is logging in from a location outside of the user&#39;s profile. This high number is utilized by the transactional rules engine  440  and/or the fraud detection system  405  to determine whether the activity is fraudulent. 
         [0085]    The transactional rules engine  440  includes a signature-based rules engine. The signature-based rules engine searches for pre-defined patterns (e.g., signatures) in the user session. The patterns include, for example, a set of conditions that characterize fraudulent activities. For example, http headers and/or http payload content are analyzed to detect variable overwriting, parameter overloading, and/or other types of activity that indicate fraudulent activity may be occurring. 
         [0086]    For example, the user  112  is submitting a request to transfer $1,000,000 from an account to an overseas bank account. The transactional rules engine  440  is monitoring the activity and examines the http payload content to ensure that the content corresponds with the information of the activity. The http payload content contains userid=GeorgeRich while the user is logged on as userid=GeorgeFraud. The user  112  is attempting to transfer funds not from his account but from another user&#39;s account by changing the userid parameter in the http payload content. The transactional rules engine  440  detects this discrepancy and classifies the user session as fraudulent. 
         [0087]    The database  450  stores elements of the user sessions in a memory module. The elements include, for example, user identification, network address, time, network path, length of time logged into the system, transactions, and/or other information contained in the user session. The memory module is, for example, volatile memory to speed the access of the stored user sessions. The memory module is, for example, persistent storage to keep for historical analysis (e.g., Oracle® Berkeley DB available from Oracle Corporation). 
         [0088]    For example, the fraud detection system  405  searches the user sessions stored in the database  450  for access by the same network address and/or network subnet. If elements of the user session resemble and/or matches elements stored in the database  450  that have been associated with fraudulent activity, then the fraud detection system  405  classifies the user session as fraudulent (e.g., stored element is that subnet 10.10.10.0 is associated with fraudulent activity, then the user session from 10.10.10.5 is classified as fraudulent). 
         [0089]    For example, the database  450  stores all of the transaction amounts for all bond purchases in volatile memory for fast access. The fraud detection system  405  receives a user session from the unification engine  320 . The user session is processed by the fraud detection system  405  to determine if fraudulent activity exists. Part of the processing by the fraud detection system  405  is comparing the transaction amount in the user session to previous transaction amounts for the bond that is being purchased. The stored elements in the database  450  for the bond being purchased includes ten years of transactions with over a hundred thousand transactions per year. The fraud detection system  405  retrieves from the database  450  in volatile memory the over one million transactions to analyze whether the transaction in the user session is fraudulent activity. The fraud detection system  405  utilizes the speed of the database  450  in volatile memory (e.g., random access memory) to process the transaction at or near the same time that the server system  122   a  or  122   b  is processing the transaction. If the transaction in the user session (e.g., purchase of 9,232 bonds) is within the normal ranges (e.g., the range of purchases is 1 to 10,342), then the fraud detection system  405  does not interdict the transaction. If the transaction in the user session (e.g., the purchase of 132,230 bonds) is not within the normal range (e.g., the range of purchases is 1 to 10,342), then the fraud detection system  405  interdict the transaction. 
         [0090]      FIG. 5  is a functional block diagram of an exemplary system  500  showing requestor systems that receive data from the data bus  210 . Parts of the exemplary system  500  are described above in association with  FIGS. 1 ,  2 ,  3 , and  4 . The data bus  210  transmits the data packets to the requestor system. In some examples, the requestor system includes a marketing analytical system  562 , a network intrusion detection system  564 , a customer service system  566 , and/or a performance analysis system  568 . 
         [0091]    The data packets received by the data bus  210  are, for example, reconstructed into data before being transmitted to the requestor system. The user requests received by the data bus  210  are, for example, unified into user sessions before being transmitted to the requestor system. The data bus  210  includes, for example, the unification engine  320  as shown in  FIG. 3  and/or the reconstruction engine  150  as shown in  FIG. 1 . 
         [0092]    The data bus  210  receives the data packets and/or the user request as encrypted information from the data collection systems  124   a  and  124   b . The data bus  210  decrypts the information before transmission to the requestor system. 
         [0093]    The data bus  210  processes the data packet and/or the user request into a format that conforms with a particular protocol. The particular protocol includes a voice over internet protocol (VoIP), a transmission control protocol (TCP), an internet protocol (IP), an extensible markup language (XML), a hypertext markup language (HTML), and/or a standard generalized markup language (SGML). 
         [0094]      FIG. 6A  is a screen shot  600   a  generated by a login module. The screen shot  600   a  shows the login screen generated by the login module that is included in the network associated with the load balancer  130  of  FIG. 1 . The login screen includes a customer identification field  602   a , a pin field  604   a , and an information submission button  606   a . The fields  602   a ,  604   a , and  606   a  are part or all of the information in the user request. 
         [0095]      FIG. 6B  is a diagram  600   b  of information sent from the transmitting device  110  of  FIG. 1  to the login module that includes the information in  FIG. 6A . The diagram  600   b  is part or all of the data packet and/or the user request. The information parameters  610   b  define how the information is routed and/or processed. The information parameters  610   b  include the source of the information, the destination of the information, routing information, the protocol for the information, and/or other types of transmitting parameters. The information data  612   b  includes the content information. The information data  612   b  includes formatting information, content information, transaction information, and/or other types of content information. The transaction information can comprise the customer id  602   b  which corresponds with the customer id field of the login screen  602   a . The transaction information can comprise the pin  604   b  which corresponds with the pin field of the login screen  604   a . The transaction information can comprise the login in  606   b  command information which corresponds with the log in submission button  606   a . The fields  602   b ,  604   b , and  606   b  are part or all of the information in the data packet and/or the information in the user request. 
         [0096]    The exemplary system  300  of  FIG. 3  unifies the user requests as illustrated by  600   a  and  600   b  into the user session. The user session is analyzed by the fraud detection system  160  to look for account access into multiple accounts. The fraud detection system  160  detects that the user  112  logging into the account previously logged into multiple other accounts in or near the same timeframe as the current login. The fraud detection system  160  determines that the access to the account is fraudulent and transmits the fraudulent activity information for the user  112  to the interdiction system  330 . The interdiction system  330  cuts off the user  112  from accessing the system  300 . 
         [0097]      FIG. 6C  is a screen shot  600   c  generated by a search module in the server system  122   a  or  122   b  of  FIG. 3 . The fields in the screen shot  600   c  can comprise part or all of the information in the user request. 
         [0098]      FIG. 6D  is a diagram  600   d  of information transmitted from the transmitting device  110  of  FIG. 3  to the search module in the server system  122   a  or  122   b  that includes the information in  FIG. 6C . The diagram  600   d  is part or all of the data packet and/or the user request. The information parameters  610   d  define how the information is routed and processed. The information data  612   d  includes the content information. 
         [0099]      FIG. 6E  is a diagram  600   e  of information received from the search module in the server system  122   a  or  122   b  of  FIG. 3 . The diagram  600   e  is part or all of the data packet and/or the user request. The information parameters  610   e  define how the information is routed and processed. The information data  612   e  includes the content information. 
         [0100]      FIG. 6F  is a screen shot  600   f  of information generated by the search module in the server system  122   a  or  122   b  of  FIG. 3 . The screen shot  600   f  shows the information received from a search module. The information shown in the screen shot  600   f  corresponds with the information  612   e  in the diagram of  FIG. 6E . 
         [0101]      FIG. 6G  is a diagram  600   g  of information transmitted to a transaction module in the server system  122   a  or  122   b  of  FIG. 3 . The diagram  600   g  is part or all of the data packet and/or the user request. The information parameters  610   g  define how the information is routed and processed. The information data  612   g  includes the content information. 
         [0102]      FIG. 6H  is a diagram  600   h  of information received from an information module in the server system  122   a  or  122   b  of  FIG. 3 . The diagram  600   h  is part or all of the data packet and/or the user request. The information parameters  610   h  define how the information is routed and processed. The information data  612   h  includes the content information. 
         [0103]      FIG. 6I  is a screen shot  600   i  of information generated by the information module in the server system  122   a  or  122   b  of  FIG. 3 . The information shown in the screen shot  600   i  corresponds with the information  612   h  in the diagram of  FIG. 6H . 
         [0104]    The diagrams of information  600   b ,  600   d ,  600   e ,  600   g , and  600   h  represent data packets and/or user requests collected at the data collection system  124   a  or  124   b  of  FIG. 1 . The reconstructed data is a combination of all of the data packets collected at the data collection systems  124   a  and  124   b . The reconstructed data is the information that is transmitted from the transmitting device  110  and from the server systems  122   a  and  122   b  (e.g., all of the raw packets that are transmitted over the system  100 ). The information included in the screenshots  600   a ,  600   c ,  600   f , and  600   i  and the diagrams of information  600   b ,  600   d ,  600   e ,  600   g , and  600   h  represent part or all of the user session. The user session is a representative of part or all of the user&#39;s activity including interactions that are not transmitted through the transmitting device  110  over the network. 
         [0105]    For example, the transmitting device  110  includes an application for interacting with the server systems  122   a  and  122   b . The interaction between the user  112  and the application on the transmitting device  110  is part of the user&#39;s activity and thus the user session includes the interactions between the application on the transmitting device  110  and the user  112 . The unification engine  320  is aware of the applications that are used on the transmitting device  110  (e.g., by keeping information about the applications and the interactions with the applications in the database  220 ) and integrates the interactions associated with the applications into the unified user session. 
         [0106]      FIG. 7  is a flowchart  700  illustrating a process of the transmission of data through the exemplary system  100  of  FIG. 1 . The user  112  transmits ( 710 ) the data packet using the transmitting device  110 . The load balancer  130  receives the data packet and sends ( 720 ) the data packet to one of the data centers,  120   a  or  120   b .  FIG. 7  includes the processing performed at data center A  120   a , illustrated as grouping  730   a  and the processing performed at data center B  120   b , illustrated as grouping  730   b . At either data center, the data packet is routed ( 732   a  and  732   b ) to the server systems  122   a  and  122   b , respectively, and the data collection systems  124   a  and  124   b  capture ( 736   a  and  736   b ), respectively, the data packets. The data packet is processed ( 734   a  and  734   b ) by the server systems  122   a  and  122   b , respectively. A response from the server systems  122   a  and  122   b  is routed back to the user and is captured ( 736   a  and  736   b ) by the data collection systems  124   a  and  124   b , respectively. The data collection systems  124   a  and  124   b  send ( 738   a  and  738   b ) the data packets to the reconstruction engine  150 . The reconstruction engine  150  receives the data packets from the data centers  120   a  and  120   b . The reconstruction engine  150  reconstructs ( 750 ) the data from the data packets. 
         [0107]    The reconstruction ( 750 ) of data packets can occur in several different ways. For example, the reconstruction ( 750 ) can include matching origination parameters between the data packets. Using, for example, the exemplary system  100  of  FIG. 1  and the parts of the data of  FIGS. 6A-6I , the reconstruction engine  150  reconstructs data packets into data. The data packets  600   b ,  600   d , and  600   g  show the information parameters  610   b ,  610   d , and  610   g . The information parameters  610   b ,  610   d , and  610   g  include the origination parameters (e.g., the source field). The source field shows that the data packets are originating from address 192.168.0.1. The origination field can be matched between the data packets by the reconstruction engine  150 . The matched data packets are transformed into hypertext markup language (HTML) using the information data  612   b ,  612   d , and  612   g  which contains HTML tags to form part or all of the data. 
         [0108]    An advantage of the exemplary system  100  is that the data packets  600   b ,  600   d , and  600   g  are routed to different data centers  130   a  and  130   b  by the load balancer  130  for processing ( 734   a  and  734   b ) by the server systems  122   a  and  122   b  and the data packets  600   b ,  600   d , and  600   g  routed to the different data centers  130   a  and  130   b  are reconstructed into part or all of the data transmitted from and received by the user&#39;s transmitting device  110 . The data packet  600   b  is routed ( 720 ) to data center A  120   a  by the load balancer  130  while the data packets  600   d  and  600   g  are routed ( 720 ) to data center B  120   b  by the load balancer  130 . The data packet  600   b  is routed ( 732   a ) to the server system  122   a  and captured ( 736   a ) by the data collection system  124   a  in data center A  120   a . The data packets  600   d  and  600   g  are routed ( 732   b ) to the server system  122   b  and captured ( 736   b ) by the data collection system  124   b  in data center B  120   b . The data collection systems  124   a  and  124   b  send ( 738   a  and  738   b ) the data packets to the reconstruction engine  150 . The reconstruction engine  150  reconstructs ( 750 ) the data packets received from data centers A  120   a  and B  120   b  into the data that the user  112  sent. Thus, the data packets are split among the data centers  120   a  and  120   b  for processing ( 734   a  and  734   b ) by the server systems  122   a  and  122   b , but are reconstructed ( 750 ) by the reconstruction engine  150 . 
         [0109]    For example, the reconstruction ( 750 ) can include matching origination and destination parameters between the data packets. Using, for example, the exemplary system  100  of  FIG. 1  and the parts of the data of  FIGS. 6A-6I , the reconstruction engine  150  reconstructs data packets into data. The data packets  600   b ,  600   d ,  600   e ,  600   g , and  600   h  show the information parameters  610   b ,  610   d ,  610   e ,  610   g , and  610   h . The information parameters  610   b ,  610   d ,  610   e ,  610   g , and  610   h  include the origination parameters (e.g., the source field, the destination field). 
         [0110]    The source field shows that the data packets as illustrated in  FIGS. 6B ,  6 D, and  6 G are originating from address 192.168.0.1. The destination field shows that the data packets  600   e  and  600   h  are sent to address 192.168.0.1. The origination field and destination fields are matched between the data packets by the reconstruction engine  150  to form the data. The matching is part or all of the reconstruction ( 750 ) of the data. 
         [0111]    An advantage of the exemplary system  100  is that the data packets  600   b ,  600   d , and  600   g  are routed to different data centers  130   a  and  130   b  by the load balancer  130  for processing ( 734   a  and  734   b ) by the server systems  122   a  and  122   b  and the data packets  600   b ,  600   d , and  600   g  routed through the different data centers  130   a  and  130   b  and the data packets transmitted by the server systems  122   a  and  122   b  are reconstructed into the data transmitted from and received by the user&#39;s transmitting device  110 . The data packet  600   b  is routed ( 720 ) to data center A  120   a  by the load balancer  130  while the data packets  600   d  and  600   g  are routed ( 720 ) to data center B  120   b  by the load balancer  130 . The data packet  600   b  is routed ( 732   a ) to the server system  122   a  and captured ( 736   a ) by the data collection system  124   a  in data center A  120   a . The data packets  600   d  and  600   g  are routed ( 732   b ) to the server system  122   b  and captured ( 736   b ) by the data collection system  124   b  in data center B  120   b . The data collection systems  124   a  and  124   b  capture ( 736   a  and  736   b ) the data packets  600   e  and  600   h  that are responses to the user  112 . The data collection systems  124   a  and  124   b  send ( 738   a  and  738   b ) the data packets to the reconstruction engine  150 . The reconstruction engine  150  reconstructs ( 750 ) the data packets received from data centers A  120   a  and B  120   b  into the data that the user  112  sent and received. Thus, the data packets are split among the data centers  120   a  and  120   b  for processing ( 734   a  and  734   b ) by the server systems  122   a  and  122   b , but are reconstructed ( 750 ) by the reconstruction engine  150 . 
         [0112]      FIG. 8  is a flowchart  800  illustrating a process of the transmission of user requests through the exemplary system  300  of  FIG. 3 . The user  112  transmits ( 810 ) the user request using the transmitting device  110 . The load balancer  130  receives the user request and sends ( 820 ) the user request to one of the data centers,  120   a  or  120   b .  FIG. 8  includes the processing performed at data center A  120   a , illustrated as grouping  830   a  and the processing performed at data center B  120   b , illustrated as grouping  830   b . At either data center, the user request is received ( 832   a  or  232   b ) at the data center  120   a  or  120   b . The user request is sent ( 834   a  or  834   b ) to the server system  122   a  or  122   b  and to the data collection system  124   a  or  124   b , respectively. The data collection systems sends ( 836   a  or  836   b ) the user request to the data bus  210 . The unification engine  320  receives ( 850 ) the user request from the data bus  210 . The unification engine  320  unifies ( 860 ) the user requests received from the data bus  210  to form the user session. 
         [0113]    The unification ( 860 ) of user requests can occur in several different ways. For example, the unification ( 860 ) includes matching origination and destination parameters between the user requests. Using, for example, the exemplary system  300  of  FIG. 3  and the parts of the user session of  FIGS. 6A-6I , the unification engine  320  unifies ( 860 ) user requests into user sessions. The user session includes the user requests  600   b ,  600   d , and  600   g , responses  600   e  and  600   h , and the information for the screenshots  600   a ,  600   c ,  600   f , and  600   i . The user requests  600   b ,  600   d , and  600   g  and responses  600   e  and  600   h  show the information parameters  610   b ,  610   d ,  610   e ,  610   g , and  610   h . The information parameters  610   b ,  610   d ,  610   e ,  610   g , and  610   h  include the origination parameters (e.g., the source field, the destination field). The source field shows that the user requests  600   b ,  600   d , and  600   g  are originating from address 192.168.0.1. The destination field shows that the user requests  600   e  and  600   h  are being sent to address 192.168.0.1. The information data  612   b ,  612   d ,  612   e ,  612   g , and  612   h  provides part or all of the information for the screenshots  600   a ,  600   c ,  600   f , and  600   i  that are part or all of the user session. The origination field and destination field can be matched between the user requests by the unification engine  320  to form the data. Based on the information stored in the database  220 , the unification engine  320  can match the information that is in the screenshots to the user requests (e.g., the unification engine can match a user request containing login information to the screenshot  600   a  of  FIG. 6  generated by the login module). The matching of the fields and the matching of the information in the screenshots are part or all of the unification ( 860 ) of the user session. 
         [0114]    An advantage of the exemplary system  300  is that the user requests  600   b ,  600   d , and  600   g  which are routed to different data centers  130   a  and  130   b  by the load balancer  130  are unified with the user requests  600   e  and  600   h  being sent to the user  112  and the information contained in the screenshots  600   a ,  600   c ,  600   f , and  600   i.    
         [0115]      FIG. 9  is a flowchart  900  illustrating a process of the transmission of user requests through the exemplary system  300  of  FIG. 3 . The user  112  using a transmitting device  110  transmits ( 910 ) the user request. The user request is sent ( 920 ) by the load balancer  130  to the data center  120   b . Data center B  120   b  is used in this example, however the load balancer can send ( 920 ) the user request to data center A  120   a . The request is routed ( 932 ) to the server system  122   b . The server system  122   b  processes ( 934 ) the request. Server system  122   b  responds ( 936 ) to the user request. The response is routed back to the user  112  through her transmitting device  110  and is captured ( 942 ) by the data collection system  124   b . The data collection system  124   b  captures ( 942 ) the user request at or near the same time as the user request is routed ( 932 ) to the server system  122   b . The data collection system  124   b  captures ( 942 ) the user request without affecting the processing ( 934 ) of the user request by the server system  122   b . After capturing ( 942 ) the user request, the data collection system  124   b  sends ( 944 ) the user request to the unification engine  320 . The unification engine  320  unifies ( 946 ) the user request to form the user session. The unification engine  320  unifies ( 946 ) the user request by itself or with other user requests to form the user session. The user session is processed ( 948 ) by the fraud detection system  160  to detect fraudulent activity. 
         [0116]    Using, for example, the exemplary system  400  of  FIG. 4  and the parts of the user session of  FIGS. 6A-6I , the fraud detection system  405  processes ( 948 ) user sessions to determine if the activity is fraudulent. The fraud detection system  405  uses the geolocation profiling engine  410 , the behavior profiling engine  420 , and the transactional rules engine  440  to process ( 948 ) the user session that includes the buy order in the user request  600   g  (i.e., 1,000,000 bonds of the fund). The geolocation profiling engine  410  processes ( 948 ) the location that the user  112  is transmitting the buy order from. The location is not suspect (e.g., the network address and network subnet are not on a list of suspect networks) based on the network address and network subnet. The geolocation profiling engine  410  generates a report of non-fraudulent activity. The behavior profiling engine  420  analyzes the buy order to determine whether the buy order is outside of the range of the behavior of the user&#39;s profile. The behavior profiling engine  420  determines that the buy order is not outside of the range of the behavior of the user  112  because by accessing the stored elements in the database  450 , the behavior profiling engine  420  determines that the user  112  has bought and sold millions of shares of bonds over the past ten years. The behavior profiling engine  420  generates a report of non-fraudulent activity. The transactional rules engine  440  analyzes the buy order to determine whether the buy order is outside of the normal range of buy and sell orders for the particular bond. The transactional rules engine  440  determines that the buying and selling of one million bonds is within the normal range for the particular bond (e.g., the bond averages one hundred million buy and sell transactions per week). The transactional rules engine  440  generates a report of non-fraudulent activity. Based on the reports of the geolocation profiling engine  410 , the behavior profiling engine  420 , and the transactional rules engine  440 , the fraud detection system  405  determines that the user session is not fraudulent. 
         [0117]      FIG. 10  is a flowchart  1000  illustrating a process of the transmission of data packets through the exemplary system  500  of  FIG. 5 . The user  112  using a transmitting device  110  transmits ( 1010 ) the data packet. The data packet is sent ( 1020 ) by the load balancer  130  to the data center B  120   b . Data center B  120   b  is used in this example, however the load balancer can send ( 1020 ) the user request to data center A  120   a . The data packet is routed ( 1032 ) to the server system  122   b . The server system  122   b  processes ( 1034 ) the data packet. Server system  122   b  responds ( 1036 ) to the data packet. The response is routed back to the user  112  through her transmitting device  110  and is captured ( 1042 ) by the data collection system  124   b . The data collection system  124   b  captures ( 1042 ) the data packet at or near the same as the data packet is routed ( 1032 ) to the server system  122   b . The data collection system  124   b  captures ( 1042 ) the data packet without affecting the processing ( 1034 ) of the data packet by the server system  122   b . After capturing ( 1042 ) the data packet, the data collection system  124   b  sends ( 1044 ) the data packet to the data bus  210 . The requestor system requests ( 1046 ) part of the data from the data bus  210 . The requested data is transmitted ( 1048 ) from the data bus  210  to the requestor system. 
         [0118]    Using, for example, the exemplary system  500  of  FIG. 5  and the parts of the data of  FIGS. 6A-6I , the requestor system (e.g.,  562 ,  564 ,  566 , and/or  568 ) requests ( 1046 ) part of the data from the data bus  210 . For this example, the requestor system is the performance analysis system  568 . The performance analysis system  568  analyzes data packets to determine the response time of the search module. The performance analysis system  568  requests ( 1046 ) the data that is sent to and from the search module. The data bus  210  transmits ( 1048 ) the requested parts of the data to the performance analysis system  568 . The parts of the data are the data packets  600   d  and  600   e  which represent the parts of the data sent to and from the search module. The performance analysis system  568  analyzes the response time between the data packet requesting the search  600   d  and the data packet that transmitted the search results  600   e . A high response time indicates that the system  500  is loaded and the performance analysis system  568  makes changes to the configuration of the load balancer  130  and/or the server systems  122   a  and  122   b  to decrease the response time. A low response time indicates that the system  500  is responding in an appropriate timeframe to the user request and no corrective action is needed. The performance analysis system  568  monitors the modules of the system  500  and makes changes to the system  500  to decrease response times when appropriate. 
         [0119]    For this example, the requestor system is the customer service system  566 . The customer service system  566  is analyzing data packets to diagnose and fix problems that the user  112  is having with the system  500 . The user  112  is attempting to search for information utilizing the information data  612   d  by submitting the information to the search module that is part of the system  500 . The search module is not returning the correct information. The user  112  contacts a customer service representative who uses the customer service system  566  to look at the data for the user  112 . The portion of the data is requested ( 1046 ) by the customer service system  566 . 
         [0120]    The customer service representative identifies that the information parameters  610   d  are not being transmitted to the system  500  correctly and therefore the information request is failing. The customer service representative instructs the user  112  to update her world wide web browser software (e.g., Mozilla® Firefox) and to try again. The user  112  updates her world wide web browser software and submits the data packet  600   d  to the system  500 . The user  112  receives the data packet response  600   e  (i.e., response to the user&#39;s request) as illustrated by the screenshot  600   f.    
         [0121]      FIG. 11  is a flowchart  1100  illustrating a process of the transmission of user requests through the exemplary system  300  of  FIG. 3 . The user  112  using a transmitting device  110  transmits ( 1110 ) the user request. The interdiction system  330  determines ( 1164 ) whether the user session should be interdicted based on notifications from the fraud detection system  160 . The fraud detection system  160  sends the notification to the interdiction system  330  when, for example, the user  112  is making transactions outside of the user&#39;s behavior profile (e.g., the user  112  is buying ten millions shares of stock when the user  112  only has one hundred shares of stock in her portfolio before this transaction). 
         [0122]    If the interdiction system  330  does not interdict ( 1164 ) the user session, then the user request is routed ( 1120 ) by the load balancer  130  to the data center  120   b . If the interdiction system  330  does interdict ( 1164 ) the user session, then access is the system  1100  is stopped ( 1166 ). Data center B  120   b  is used in this example, however the load balancer can send ( 920 ) the user request to data center A  120   a . The request is routed ( 1032 ) to the server system  122   b . The server system  122   b  processes ( 1034 ) the request. Server system  122   b  responds ( 1036 ) to the user request. The response is routed back to the user  112  through her transmitting device  110  unless the interdiction system  330  determines ( 1162 ) that the user session should be interdicted based on notifications from the fraud detection system  160 . If the interdiction system  330  does interdict ( 1162 ) the user session, then access is the system  1100  is stopped ( 1166 ) (i.e., the response to the user request is not sent back to the user  112 ). 
         [0123]    The data collection system  124   b  captures ( 1142 ) the user request at or near the same as the user request is routed ( 1132 ) to the server system  122   b . The data collection system  124   b  captures ( 1142 ) the user request without affecting the processing ( 1134 ) of the user request by the server system  122   b . After capturing ( 1142 ) the user request, the data collection system  124   b  sends ( 1144 ) the user request to the data bus  210 . The unification engine  320  receives ( 1146 ) the user request from the data bus  210 . The unification engine  320  unifies ( 1148 ) the user request to form the user session. The unification engine  320  unifies ( 1148 ) the user request by itself or with other user requests to form the user session. The user session is processed ( 1050 ) by the fraud detection system  160  to detect fraudulent activity. If the fraud detection system  160  detects ( 1160 ) fraudulent activity, then the fraud detection system  160  notifies the interdiction system  330 . If the fraud detection system  160  does not detect ( 1160 ) fraudulent activity, then the fraud detection system  160  continues processing ( 1150 ) user sessions looking for fraudulent activity. 
         [0124]    Using, for example the exemplary system  400  of  FIG. 4  and the parts of the user session of  FIGS. 6A-6I , the interdiction system  330  determines ( 1162  and  1164 ) whether to interdict user requests and responses to user requests. The unification ( 1148 ) of user requests into user sessions by the unification engine  320  is described above. The user session is processed ( 1150 ) by the fraud detection system  405  to look for search terms that are categorized as suspect. The fraud detection system  405  uses the behavior profiling engine  420  to process ( 1150 ) the user session. The behavior profiling engine  420  processes ( 1150 ) the search terms in the information data  612   d  to determine if the search terms are categorized by the behavior profiling engine  420  as suspect. The search terms for a high yield bond, fund performance of ten years or greater, and a Morningstar rating of five stars is not classified as a suspect search term by the behavior profiling engine  420  because the search is categorized as a long term investment search and not a fraudulent search. Therefore, the behavior profiling engine  420  will not classify the search by the user  112  as fraudulent activity and the fraud detection system  160  will not notify ( 1160 ) the interdiction system  330  since no fraudulent activity was detected. 
         [0125]      FIG. 12  is a flowchart  1200  illustrating a process of the transmission of user requests through the exemplary system  400  of  FIG. 4 . The process of the transmission of user requests is similar in parts to  FIG. 11  as described above. The user  112  using a transmitting device  110  transmits ( 1110 ) the user request. The user request is sent to the authentication system that authenticates ( 1270 ) the user  112  to verify that only user requests from a properly authenticated user  112  are allowed to access the system  1200 . If the user  112  is granted ( 1268 ) access to the system  1200 , then the user request is transmitted to the load balancer  130 . If the user  112  is not granted ( 1268 ) access to the system  1200 , then the user access is stopped ( 1166 ). 
         [0126]    In some examples, the authentication system includes a computer, a network hub, a network switch, a network router, a network firewall, an authentication server (e.g., Kerberos authentication server in Windows® Server 2003 available from Microsoft Corporation, Oracle® Access Manager available from Oracle Corporation), and/or other authentication modules. 
         [0127]      FIG. 13  is a flowchart  1300  illustrating a process of the transmission of user requests through the exemplary system  400  of  FIG. 4 . The process of the transmission of user requests is similar in parts to  FIGS. 11 and 12  as described above. If the fraud detection system  405  detects ( 1160 ) fraudulent activity, then the interdiction system  330  generates ( 1382 ) an authentication rule based on the fraudulent activity. The authentication rule is communicated ( 1384 ) to the authentication system. When the user  112  transmits ( 1110 ) the user request, then the authentication system will have the rule when authenticating ( 1270 ) the user  112 . In some examples, the rule includes the identification of a certain location that is not allowed access to the exemplary system, the identification of a certain user account that is not allowed access to the exemplary system, and/or the identification of a certain user account that is not allowed access to the exemplary system for a set amount of time. 
         [0128]      FIG. 14  is a flowchart  1400  illustrating a process of the transmission of user requests through the exemplary system  400  of  FIG. 4 . The process of the transmission of user requests is similar in parts to  FIGS. 11 ,  12 , and  13  as described above. If the fraud detection system  405  detects ( 1160 ) fraudulent activity, then the interdiction system  330  interdicts ( 1162  and  1164 ) the user requests and the responses to the user requests for the user session in which fraud was detected. The interdiction system  330  then redirects ( 1470 ) the user requests and responses to the user requests to a separate network. In some examples, the separate network includes a honeypot server, a honeypot network, a server system, a network firewall, a network router, a network hub, a network switch, and/or other network communication devices. The separate network is, for example, used to allow the user  115  to continue the fraudulent activity without adversely affecting the data centers  120   a  and  120   b . The ability to allow the user  115  to continue committing the fraudulent activity on the separate network allows for the analysis of the fraudulent activity for the addition or modification of rules and/or profiles for the fraud detection system  405 . 
         [0129]    The above-described systems and methods can be implemented in digital electronic circuitry, in computer hardware, firmware, and/or software. The implementation can be as a computer program product (i.e., a computer program tangibly embodied in an information carrier). The implementation can, for example, be in a machine-readable storage device and/or in a propagated signal, for execution by, or to control the operation of, data processing apparatus. The implementation can, for example, be a programmable processor, a computer, and/or multiple computers. 
         [0130]    A computer program can be written in any form of programming language, including compiled and/or interpreted languages, and the computer program can be deployed in any form, including as a stand-alone program or as a subroutine, element, and/or other unit suitable for use in a computing environment. A computer program can be deployed to be executed on one computer or on multiple computers at one site. 
         [0131]    Method steps can be performed by one or more programmable processors executing a computer program to perform functions of the invention by operating on input data and generating output. Method steps can also be performed by and an apparatus can be implemented as special purpose logic circuitry. The circuitry can, for example, be a FPGA (field programmable gate array) and/or an ASIC (application-specific integrated circuit). Modules, subroutines, and software agents can refer to portions of the computer program, the processor, the special circuitry, software, and/or hardware that implements that functionality. 
         [0132]    Processors suitable for the execution of a computer program include, by way of example, both general and special purpose microprocessors, and any one or more processors of any kind of digital computer. Generally, a processor receives instructions and data from a read-only memory or a random access memory or both. The essential elements of a computer are a processor for executing instructions and one or more memory devices for storing instructions and data. Generally, a computer can include, can be operatively coupled to receive data from and/or transfer data to one or more mass storage devices for storing data (e.g., magnetic, magneto-optical disks, or optical disks). 
         [0133]    Data transmission and instructions can also occur over a communications network. Information carriers suitable for embodying computer program instructions and data include all forms of non-volatile memory, including by way of example semiconductor memory devices. The information carriers can, for example, be EPROM, EEPROM, flash memory devices, magnetic disks, internal hard disks, removable disks, magneto-optical disks, CD-ROM, and/or DVD-ROM disks. The processor and the memory can be supplemented by, and/or incorporated in special purpose logic circuitry. 
         [0134]    To provide for interaction with a user, the above described techniques can be implemented on a computer having a display device. The display device can, for example, be a cathode ray tube (CRT) and/or a liquid crystal display (LCD) monitor. The interaction with a user can, for example, be a display of information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer (e.g., interact with a user interface element). Other kinds of devices can be used to provide for interaction with a user. Other devices can, for example, be feedback provided to the user in any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback). Input from the user can, for example, be received in any form, including acoustic, speech, and/or tactile input. 
         [0135]    The above described techniques can be implemented in a distributed computing system that includes a back-end component. The back-end component can, for example, be a data server, a middleware component, and/or an application server. The above described techniques can be implemented in a distributing computing system that includes a front-end component. The front-end component can, for example, be a client computer having a graphical user interface, a Web browser through which a user can interact with an example implementation, and/or other graphical user interfaces for a transmitting device. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), the Internet, wired networks, and/or wireless networks. 
         [0136]    The system can include clients and servers. A client and a server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. 
         [0137]    Comprise, include, and/or plural forms of each are open ended and include the listed parts and can include additional parts that are not listed. And/or is open ended and includes one or more of the listed parts and combinations of the listed parts. 
         [0138]    One skilled in the art will realize the invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The foregoing embodiments are therefore to be considered in all respects illustrative rather than limiting of the invention described herein. Scope of the invention is thus indicated by the appended claims, rather than by the foregoing description, and all changes that come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein.