Abstract:
Methods and systems for operation upon one or more data processors for aggregating reputation data from dispersed reputation engines and deriving global reputation information for use in handling received communications.

Description:
CROSS-REFERENCE  
       [0001]     This application claims priority to U.S. patent application Ser. No. 11/142,943, entitled “Systems and Methods for Classification of Messaging Entities,” filed on Jun. 2, 2005, and to U.S. patent application Ser. No. 11/173,941, entitled “Message Profiling Systems and Methods,” filed on Jul. 1, 2005. 
     
    
       [0002]     This application incorporates by reference, in their entirety and for all purposes, commonly assigned U.S. patent applications:  
                                       Application               No.   Title   Filing Date                   10/094,211   “Systems and Methods for Enhancing   Mar. 8, 2002           Electronic Communication Security”       10/361,067   “Systems and Methods for Automated   Feb. 7, 2003           Whitelisting in Monitored           Communications”       10/373,325   “Systems and Methods for Upstream   Feb. 24, 2003           Threat Pushback”       10/384,924   “Systems and Methods for Secure   Mar. 6, 2003           Communication Delivery”       11/142,943   “Systems and Methods for   Jun. 2, 2005           Classification of Messaging           Entities”       11/173,941   “Message Profiling Systems and   Jul. 1, 2005           Methods”       11/388,575   “Systems and Methods for Message   Mar. 24, 2006           Threat Management”       11/456,803   “Systems And Methods For Adaptive   Jul. 11, 2006           Message Interrogation Through           Multiple Queues”       11/456,765   “Systems and Methods For Anomaly   Jul. 11, 2006           Detection in Patterns of Monitored           Communications”       11/423,313   “Systems and Methods for   Jun. 9, 2006           Identifying Potentially Malicious           Messages”       11/456,954   “Systems and Methods For Message   Jul. 12, 2006           Threat Management”       11/456,960   “Systems and Methods For Message   Jul. 12, 2006           Threat Management”       11/423,308   “Systems and Methods for   Jun. 9, 2006           Graphically Displaying Messaging           Traffic”       11/383,347   “Content-Based Policy Compliance   May 15, 2006           Systems and Methods”       11/423,329   “Methods and Systems for Exposing   Jun. 9, 2006           Messaging Reputation to an End User”                  
 
         [0003]     This application incorporates by reference, in their entirety and for all purposes, commonly assigned U.S. Patents:  
                                       Patent No.   Title   Filing Date                   6,941,467   “Systems and Methods for Adaptive   Mar. 8, 2002           Message Interrogation through Multiple           Queues”       7,089,590   “Systems and Methods for Adaptive   Sep. 2, 2005           Message Interrogation through Multiple           Queues”       7,096,498   “Systems and Methods for Message   Feb. 7, 2003           Threat Management”       7,124,438   “Systems and Methods for Anomaly   Mar. 8, 2002           Detection in Patterns of Monitored           Communications”                  
 
       Technical Field  
       [0004]     This document relates generally to systems and methods for processing communications and more particularly to systems and methods for classifying entities associated with communications.  
       BACKGROUND  
       [0005]     In the anti-spam industry, spammers use various creative means for evading detection by spam filters. As such, the entity from which a communication originated can provide another indication of whether a given communication should be allowed into an enterprise network environment.  
         [0006]     However, current tools for message sender analysis include internet protocol (IP) blacklists (sometimes called real-time blacklists (RBLs)) and IP whitelists (real-time whitelists (RWLs)). Whitelists and blacklists certainly add value to the spam classification process; however, whitelists and blacklists are inherently limited to providing a binary-type (YES/NO) response to each query. Moreover, blacklists and whitelists treat entities independently, and overlook the evidence provided by various attributes associated with the entities.  
       SUMMARY  
       [0007]     Systems and methods used to aggregate reputation information are provided. Systems used to aggregate reputation information can include a centralized reputation engine and an aggregation engine. The centralized reputation engine can receive feedback from a plurality of local reputation engines. The aggregation engine can derive a global reputation for a queried entity based upon an aggregation of the plurality of local reputations. The centralized reputation engine can further provide the global reputation of the queried entity to a local reputation engines responsive to receiving a reputation query from the local reputation engine.  
         [0008]     Methods of aggregating reputation information can include: receiving a reputation query from a requesting local reputation engine; retrieving a plurality of local reputations the local reputations being respectively associated with a plurality of local reputation engines; aggregating the plurality of local reputations; deriving a global reputation from the aggregation of the local reputations; and, responding to the reputation query with the global reputation.  
         [0009]     Examples of computer readable media operating on a processor aggregate local reputation data to produce a global reputation vector, can perform the steps of: receiving a reputation query from a requesting local reputation engine; retrieving a plurality of local reputations the local reputations being respectively associated with a plurality of local reputation engines; aggregating the plurality of local reputations; deriving a global reputation from the aggregation of the local reputations; and, responding to the reputation query with the global reputation.  
         [0010]     Other example reputation aggregations systems can include a communications interface and a reputation engine. The communications interface can receive global reputation information from a central server, the global reputation being associated with an entity. The reputation engine can bias the global reputation received from the central server based upon defined local preferences.  
         [0011]     Further example reputation aggregation systems can include a communications interface, a reputation module and a traffic control module. The communications interface can receive distributed reputation information from distributed reputation engines. The reputation module can aggregate the distributed reputation information and derive a global reputation based upon the aggregation of the distributed reputation information, the reputation module can also derive a local reputation information based upon communications received by the reputation module. The traffic control module can determine handling associated with communications based upon the global reputation and the local reputation. 
     
    
     DESCRIPTION OF DRAWINGS  
       [0012]      FIG. 1  is a block diagram depicting an example network in which systems and methods of this disclosure can operate.  
         [0013]      FIG. 2  is a block diagram depicting an example network architecture of this disclosure.  
         [0014]      FIG. 3  is a block diagram depicting an example of communications and entities including identifiers and attributes used to detect relationships between entities.  
         [0015]      FIG. 4  is a flowchart depicting an operational scenario used to detect relationships and assign risk to entities.  
         [0016]      FIG. 5  is a block diagram illustrating an example network architecture including local reputations stored by local security agents and a global reputation stored by one or more servers.  
         [0017]      FIG. 6  is a block diagram illustrating a determination of a global reputation based on local reputation feedback.  
         [0018]      FIG. 7  is a flow diagram illustrating an example resolution between a global reputation and a local reputation.  
         [0019]      FIG. 8  is an example graphical user interface for adjusting the settings of a filter associated with a reputation server.  
         [0020]      FIG. 9  is a block diagram illustrating reputation based connection throttling for voice over internet protocol (VoIP) or short message service (SMS) communications.  
         [0021]      FIG. 10  is a block diagram illustrating a reputation based load balancer.  
         [0022]      FIG. 11A  is a flowchart illustrating an example operational scenario for geolocation based authentication.  
         [0023]      FIG. 11B  is a flowchart illustrating another example operational scenario for geolocation based authentication.  
         [0024]      FIG. 11C  is a flowchart illustrating another example operational scenario for geolocation based authentication.  
         [0025]      FIG. 12  is a flowchart illustrating an example operational scenario for a reputation based dynamic quarantine.  
         [0026]      FIG. 13  is an example graphical user interface display of an image spam communication.  
         [0027]      FIG. 14  is a flowchart illustrating an example operational scenario for detecting image spam.  
         [0028]      FIG. 15A  is a flowchart illustrating an operational scenario for analyzing the structure of a communication.  
         [0029]      FIG. 15B  is a flowchart illustrating an operational scenario for analyzing the features of an image.  
         [0030]      FIG. 15C  is a flowchart illustrating an operational scenario for normalizing the an image for spam processing.  
         [0031]      FIG. 15D  is a flowchart illustrating an operational scenario for analyzing the fingerprint of an image to find common fragments among multiple images.  
     
    
     DETAILED DESCRIPTION  
       [0032]      FIG. 1  is a block diagram depicting an example network environment in which systems and methods of this disclosure can operate. Security agent  100  can typically reside between a firewall system (not shown) and servers (not shown) internal to a network  110  (e.g., an enterprise network). As should be understood, the network  110  can include a number of servers, including, for example, electronic mail servers, web servers, and various application servers as may be used by the enterprise associated with the network  110 .  
         [0033]     The security agent  100  monitors communications entering and exiting the network  110 . These communications are typically received through the internet  120  from many entities  130   a - f  that are connected to the internet  120 . One or more of the entities  130   a - f  can be legitimate originators of communications traffic. However, one or more of the entities  130   a - f  can also be non-reptutable entities originating unwanted communications. As such, the security agent  100  includes a reputation engine. The reputation engine can inspect a communication and to determine a reputation associated with an entity that originated the communication. The security agent  100  then performs an action on the communication based upon the reputation of the originating entity If the reputation indicates that the originator of the communication is reputable, for example, the security agent can forward the communication to the recipient of the communication. However, if the reputation indicates that the originator of the communication is non-reputable, for example, the security agent can quarantine the communication, perform more tests on the message, or require authentication from the message originator, among many others. Reputation engines are described in detail in U.S. Patent Publication No. 2006/0015942, which is hereby incorporated by reference.  
         [0034]      FIG. 2  is a block diagram depicting an example network architecture of this disclosure. Security agents  100   a - n  are shown logically residing between networks  110   a - n , respectively, and the internet  120 . While not shown in  FIG. 2 , it should be understood that a firewall may be installed between the security agents  100   a - n  and the internet  120  to provide protection from unauthorized communications from entering the respective networks  110   a - n . Moreover, intrusion detection systems (IDS) (not shown) can be deployed in conjunction with firewall systems to identify suspicious patterns of activity and to signal alerts when such activity is identified.  
         [0035]     While such systems provide some protection for a network they typically do not address application level security threats. For example, hackers often attempt to use various network-type applications (e.g., e-mail, web, instant messaging (IM), etc.) to create a pre-textual connection with the networks  110   a - n  in order to exploit security holes created by these various applications using entities  130   a - e . However, not all entities  130   a - e  imply threats to the network  110   a - n . Some entities  130   a - e  originate legitimate traffic, allowing the employees of a company to communicate with business associates more efficiently. While examining the communications for potential threats is useful, it can be difficult to maintain current threat information because attacks are being continually modified to account for the latest filtering techniques. Thus, security agents  100   a - n  can run multiple tests on a communication to determine whether the communication is legitimate.  
         [0036]     Furthermore, sender information included in the communication can be used to help determine whether or not a communication is legitimate. As such, sophisticated security agents  100   a - n  can track entities and analyze the characteristics of the entities to help determine whether to allow a communication to enter a network  110   a - n . The entities  110   a - n  can then be assigned a reputation. Decisions on a communication can take into account the reputation of an entity  130   a - e  that originated the communication. Moreover, one or more central systems  200  can collect information on entities  120   a - e  and distribute the collected data to other central systems  200  and/or the security agents  100   a - n.    
         [0037]     Reputation engines can assist in identifying the bulk of the malicious communications without extensive and potentially costly local analysis of the content of the communication. Reputation engines can also help to identify legitimate communications and prioritize their delivery and reduce the risk of misclassifying a legitimate communication. Moreover, reputation engines can provide a dynamic and predictive approaches to the problem of identifying malicious, as well as legitimate, transactions in physical or virtual worlds. Examples include the process of filtering malicious communications in an email, instant messaging, VoIP, SMS or other communication protocol system using analysis of the reputation of sender and content. A security agent  100   a - n  can then apply a global or local policy to determine what action to perform with respect to the communication (such as deny, quarantine, load balance, deliver with assigned priority, analyze locally with additional scrutiny) to the reputation result.  
         [0038]     However, the entities  130   a - e  can connect to the internet in a variety of methods. As should be understood, an entity  130   a - e  can have multiple identifiers (such as, for example, e-mail addresses, IP addresses, identifier documentation, etc) at the same time or over a period of time. For example, a mail server with changing IP addresses can have multiple identities over time. Moreover, one identifier can be associated with multiple entities, such as, for example, when an IP address is shared by an organization with many users behind it. Moreover, the specific method used to connect to the internet can obscure the identification of the entity  130   a - e . For example, an entity  130   b  may connect to the internet using an internet service provider (ISP)  200 . Many ISPs  200  use dynamic host configuration protocol (DHCP) to assign IP addresses dynamically to entities  130   b  requesting a connection. Entities  130   a - e  can also disguise their identity by spoofing a legitimate entity. Thus, collecting data on the characteristics of each entity  130   a - e  can help to categorize an entity  130   a - e  and determine how to handle a communication.  
         [0039]     The case of creation and spoofing of identities in both virtual and physical world can create an incentive for users to act maliciously without bearing the consequences of that act. For example, a stolen IP address on the Internet (or a stolen passport in the physical world) of a legitimate entity by a criminal can enable that criminal to participate in malicious activity with relative ease by assuming the stolen identity. However, by assigning a reputation to the physical and virtual entities and recognizing the multiple identities that they can employ, reputation systems can influence reputable and non-reputable entities to operate responsibly for fear of becoming non-reputable, and being unable to correspond or interact with other network entities.  
         [0040]      FIG. 3  is a block diagram depicting an example of communications and entities including using identifiers and attributes used to detect relationships between entities. Security agents  100   a - b  can collect data by examining communications that are directed to an associated network. Security agents  100   a - b  can also collect data by examining communications that are relayed by an associated network. Examination and analysis of communications can allow the security agents  100   a - b  to collect information about the entities  300   a - c  sending and receiving messages, including transmission patterns, volume, or whether the entity has a tendency to send certain kinds of message (e.g., legitimate messages, spam, virus, bulk mail, etc.), among many others.  
         [0041]     As shown in  FIG. 3 , each of the entities  300   a - c  is associated with one or more identifiers  310   a - c , respectively. The identifiers  310   a - c  can include, for example, IP addresses, universal resource locator (URL), phone number, IM username, message content, domain, or any other identifier that might describe an entity. Moreover, the identifiers  310   a - c  are associated with one or more attributes  320   a - c . As should be understood, the attributes  320   a - c  are fitted to the particular identifier  310   a - c  that is being described. For example, a message content identifier could include attributes such as, for example, malware, volume, type of content, behavior, etc. Similarly, attributes  320   a - c  associated with an identifier, such as IP address, could include one or more IP addresses associated with an entity  300   a - c.    
         [0042]     Furthermore, it should be understood that this data can be collected from communications  330   a - c  (e.g., e-mail) typically include some identifiers and attributes of the entity that originated the communication. Thus, the communications  330   a - c  provide a transport for communicating information about the entity to the security agents  100   a ,  100   b . These attributes can be detected by the security agents  100   a ,  100   b  through examination of the header information included in the message, analysis of the content of the message, as well as through aggregation of information previously collected by the security agents  100   a ,  100   b  (e.g., totaling the volume of communications received from an entity).  
         [0043]     The data from multiple security agents  100   a ,  100   b  can be aggregated and mined. For example, the data can be aggregated and mined by a central system which receives identifiers and attributes associated with all entities  300   a - c  for which the security agents  100   a ,  100   b  have received communications. Alternatively, the security agents  100   a ,  100   b  can operate as a distributed system, communicating identifier and attribute information about entities  300   a - c  with each other. The process of mining the data call correlate the attributes of entities  300   a - c  with each other, thereby determining relationships between entities  300   a - c  (such as, for example, correlations between an event occurrence, volume, and/or other determining factors).  
         [0044]     These relationships can then be used to establish a multi-dimensional reputation “vector” for all identifiers based on the correlation of attributes that have been associated with each identifier. For example, if a non-reputable entity  300   a  with a known reputation for being non-reputable sends a message  330   a  with a first set of attributes  350   a , and then an unknown entity  300   b  sends a message  330   b  with a second set of attributes  350   b , the security agent  100   a  can determine whether all or a portion of the first set of attributes  350   a  matched all or a portion of the second set of attributes  350   b . When some portion of the first set of attributes  350   a  matches some portion of the second set of attributes  330   b , a relationship can be created depending upon the particular identifier  320   a ,  3   b  that included the matching attributes  330   a ,  330   b . The particular identifiers  340   a ,  340   b  which are found to have matching attributes can be used to determine a strength associated with the relationship between the entities  300   a ,  300   b . The strength of the relationship can help to determine how much of the non-reputable qualities of the non-reputable entity  300   a  are attributed to the reputation of the unknown entity  300   b.    
         [0045]     However, it should also be recognized that the unknown entity  300   b  may originate a communication  330   c  which includes attributes  350   c  that match some attributes  350   d  of a communication  330   d  originating from a known reputable entity  300   c . The particular identifiers  340   c ,  340   d  which are found to have matching attributes can be used to determine a strength associated with the relationship between the entities  300   b ,  300   c . The strength of the relationship can help to determine how much of the reputable qualities of reputable entity  300   c  are attributed to the reputation of the unknown entity  300   b.    
         [0046]     A distributed reputation engine also allows for real-time collaborative sharing of global intelligence about the latest threat landscape, providing instant protection benefits to the local analysis that can be performed by a filtering or risk analysis system, as well as identify malicious sources of potential new threats before they even occur. Using sensors positioned at many different geographical locations information about new threats can be quickly and shared with the central system  200 , or with the distributed security agents  100   a ,  100   b . As should be understood, such distributed sensors can include the local security agents  100   a ,  100   b , as well as local reputation clients, traffic monitors, or any other device suitable for collecting communication data (e.g., switches, routers, servers, etc.).  
         [0047]     For examples security agents  100   a ,  100   b  can communicate with a central system  200  to provide sharing of threat and reputation information. Alternatively, the security agents  100   a ,  100   b  can communicate threat and reputation information between each other to provide up to date and accurate threat information. In the example of  FIG. 3 , the first security agent  100   a  has information about the relationship between the unknown entity  300   b  and the non-reputable entity  300   a , while the second security agent  100   b  has information about the relationship between the unknown entity  300   b  and the reputable entity  300   c . Without sharing the information, the first security agent  100   a  may take a particular action on the communication based upon the detected relationship. However, with the knowledge of the relationship between the unknown entity  300   b  and the reputable entity  300   c , the first security agent  100   a  might take a different action with a received communication from the unknown entity  300   b . Sharing of the relationship information between security agents, thus provides for a more complete set of relationship information upon which a determination will be made.  
         [0048]     The system attempts to assign reputations (reflecting a general disposition and/or categorization) to physical entities, such as individuals or automated systems performing transactions. In the virtual world, entities are represented by identifiers (ex. IPs, URLs, content) that are tied to those entities in the specific transactions (such as sending a message or transferring money out of a bank account) that the entities are performing. Reputation can thus be assigned to those identifiers based on their overall behavioral and historical patterns as well as their relationship to other identifiers, such as the relationship of IPs sending messages and URLs included in those messages. A “bad” reputation for a single identifier can cause the reputation of other neighboring identifiers to worsen, if there is a strong correlation between the identifiers. For example, an IP that is sending URLs which have a bad reputation will worsen its own reputation because of the reputation of the URLs. Finally, the individual identifier reputations can be aggregated into a single reputation (risk score) for the entity that is associated with those identifiers  
         [0049]     It should be noted that attributes can fall into a number of categories. For example, evidentiary attributes can represent physical, digital, or digitized physical data about an entity. This data can be attributed to a single known or unknown entity, or shared between multiple entities (forming entity relationships). Examples of evidentiary attributes relevant to messaging security include IP (internet protocol) address, known domain names, URLs, digital fingerprints or signatures used by the entity, TCP signatures, and etcetera.  
         [0050]     As another example, behavioral attributes can represent human or machine-assigned observations about either an entity or an evidentiary attribute. Such attributes may include one, many, or all attributes from one or more behavioral profiles. For example, a behavioral attribute generically associated with a spammer may by a high volume of communications being sent from that entity.  
         [0051]     A number of behavioral attributes for a particular type of behavior can be combined to derive a behavioral profile. A behavioral profile can contain a set of predefined behavioral attributes. The attributive properties assigned to these profiles include behavioral events relevant to defining the disposition of an entity matching the profile. Examples of behavioral profiles relevant to messaging security might include, “Spammer”, “Scammer”, and “Legitimate Sender”. Events and/or evidentiary attributes relevant to each profile define appropriate entities to which a profile should be assigned. This may include a specific set of sending patterns, blacklist events, or specific attributes of the evidentiary data. Some examples include: Sender/Receiver Identification; Time Interval and sending patterns; Severity and disposition of payload; Message construction; Message quality; Protocols and related signatures; Communications medium  
         [0052]     It should be understood that entities sharing some or all of the same evidentiary attributes have an evidentiary relationship. Similarly, entities sharing behavioral attributes have a behavioral relationship. These relationships help form logical groups of related profiles, which can then be applied adaptively to enhance the profile or identify entities slightly more or less standard with the profiles assigned.  
         [0053]      FIG. 4  is a flowchart depicting an operational scenario  400  used to detect relationships and assign risk to entities. The operational scenario begins at step  410  by collecting network data. Data collection can be done, for example, by a security agent  100 , a client device, a switch, a router, or any other device operable to receive communications from network entities (e.g., e-mail servers, web servers, IM servers, ISPs, file transfer protocol (FTP) servers, gopher servers, VoIP equipments, etc.).  
         [0054]     At step  420  identifiers are associated with the collected data (e.g., communication data). Step  420  can be performed by a security agent  100  or by a central system  200  operable to aggregate data from a number of sensor devices including, for example, one or more security agents  100 . Alternatively, step  420  can be performed by the security agents  100  themselves. The identifiers can be based upon the type of communication received. For example, an e-mail can include one set of information (e.g., IP address of originator and destination, text content, attachment, etc.), while a VoIP communication can include a different set of information (e.g., originating phone number (or IP address if originating from a VoIP client), receiving phone number (or IP address if destined for a VoIP phone), voice content, etc.). Step  420  can also include assigning the attributes of the communication with the associated identifiers.  
         [0055]     At step  430  the attributes associated with the entities are analyzed to determine whether any relationships exist between entities for which communications information has been collected. Step  430  can be performed, for example, by a central system  200  or one or more distributed security agents  100 . The analysis can include comparing attributes related to different entities to find relationships between the entities. Moreover, based upon the particular attribute which serves as the basis for the relationship, a strength can be associated with the relationship.  
         [0056]     At step  440  a risk vector is assigned to the entities. As an example, the risk vector can be assigned by the central system  200  or by one or more security agents  100 . The risk vector assigned to an entity  130  ( FIGS. 1-2 ),  300  ( FIG. 3 ) can be based upon the relationship found between the entities and on the basis of the identifier which formed the basis for the relationship.  
         [0057]     At step  450 , an action can be performed based upon the risk vector. The action can be performed, for example, by a security agent  100 . The action can be performed on a received communication associated with all entity for which a risk vector has been assigned. The action can include any of allow, deny, quarantine, load balance, deliver with assigned priority, or analyze locally with additional scrutiny, among many others. However, it should be understood that a reputation vector can be derived separately  
         [0058]      FIG. 5  is a block diagram illustrating an example network architecture including local reputations  500   a - e  derived by local reputation engines  510   a - e  and a global reputation  520  stored by one or more servers  530 . The local reputation engines  510   a - e , for example, can be associated with local security agents such as security agents  100 . Alternatively, the local reputation engines  510   a - e  can be associated, for example, with a local client. Each of the reputation engines  510   a - e  includes a list of one or more entities for which the reputation engine  510   a - e  stores a derived reputation  500   a - e.    
         [0059]     However, these stored derived reputations can be inconsistent between reputation engines, because each of the reputation engines may observe different types of traffic. For example, reputation engine  1   510   a  may include a reputation that indicates a particular entity is reputable, while reputation engine  2   510   b  may include a reputation that indicates that the same entity is non-reputable. These local reputational inconsistencies can be based upon different traffic received from the entity. Alternatively, the inconsistencies can be based upon the feedback from a user of local reputation engine  1   510   a  indicating a communication is legitimate, while a user of local reputation engine  2   510   b  provides feedback indicating that the same communication is not legitimate.  
         [0060]     The server  530  receives reputation information from the local reputation engines  510   a - e . However, as noted above, some of the local reputation information may be inconsistent with other local reputation information. The server  530  can arbitrate between the local reputations  500   a - e  to determine a global reputation  520  based upon the local reputation information  500   a - e . In some examples, the global reputation information  520  can then be provided back to the local reputation engines  510   a - e  to provide these local engines  500   a - e  with up-to-date reptutational information. Alternative, the local reputation engines  510   a - e  can be operable to query the server  530  for reputation information. In some examples, the server  530  responds to the query with global reputation information  520 .  
         [0061]     In other examples, the server  530  applies a local reputation bias to the global reputation  520 . The local reputation bias can perform a transform on the global reputation to provide the local reputation engines  510   a - e  with a global reputation vector that is biased based upon the preferences of the particular local reputation engine  510   a - e  which originated the query Thus, a local reputation engine  510   a  with an administrator or user(s) that has indicated a High tolerance for spam messages can receive a global reputation vector that accounts for an indicated tolerance. The particular components of the reputation vector returns to the reputation engine  510   a  might include portions of the reputation vector that are deemphasized with relationship to the rest of the reputation vector. Likewise, a local reputation engine  510   b  that has indicated, for example, a low tolerance communications from entities with reputations for originating viruses may receive a reputation vector that amplifies the components of the reputation vector that relate to virus reputation.  
         [0062]      FIG. 6  is a block diagram illustrating a determination of a global reputation based on local reputation feedback. A local reputation engine  600  is operable to send a query through a network  610  to a server  620 . In some examples, the local reputation engine  600  originates a query in response to receiving a communication from an unknown entity Alternatively the local reputation engine  600  can originate the query responsive to receiving any communications, thereby promoting use of more up-to-date reputation information.  
         [0063]     The server  620  is operable to respond to the query with a global reputation determination. The central server  620  can derive the global reputation using a global reputation aggregation engine  630 . The global reputation aggregation engine  630  is operable to receive a plurality of local reputations  640  from a respective plurality of local reputation engines. In some examples, the plurality of local reputations  640  can be periodically sent by the reputation engines to the server  620 . Alternatively, the plurality of local reputations  640  can be retrieved by the server upon receiving a query from one of the local reputation engines  600 .  
         [0064]     The local reputations can be combined using confidence values related to each of the local reputation engines and then accumulating the results. The confidence value can indicate the confidence associated with a local reputation produced by an associated reputation engine. Reputation engines associated with individuals, for example, can receive a lower weighting in the global reputation determination. In contrast, local reputations associated with reputation engines operating on large networks can receive greater weight in the global reputation determination based upon the confidence value associated with that reputation engine.  
         [0065]     In some examples, the confidence values  650  can be based upon feedback received from users. For example, a reputation engine that receives a lot of feedback indicating that communications were not properly handled because local reputation information  640  associated with the communication indicated the wrong action can be assigned low confidence values  650  for local reputations  640  associated with those reputation engines. Similarly, reputation engines that receive feedback indicating that the communications were handled correctly based upon local reputation information  640  associated with the communication indicated the correct action can be assigned a high confidence value  650  for local reputations  640  associated with the reputation engine. Adjustment of the confidence values associated with the various reputation engines can be accomplished using a tuner  660 , which is operable to receive input information and to adjust the confidence values based upon the received input. In some examples, the confidence values  650  can be provided to the server  620  by the reputation engine itself based upon stored statistics for incorrectly classified entities. In other examples, information used to weight the local reputation information can be communicated to the server  620 .  
         [0066]     In some examples, a bias  670  can be applied to the resulting global reputation vector. The bias  670  can normalize the reputation vector to provide a normalized global reputation vector to a reputation engine  600 . Alternatively, the bias  670  can be applied to account for local preferences associated with the reputation engine  600  originating the reputation query. Thus, a reputation engine  600  can receive a global reputation vector matching the defined preferences of the querying reputation engine  600 . The reputation engine  600  can take an action on the communication based upon the global reputation vector received from the server  620 .  
         [0067]      FIG. 7  is a block diagram illustrating an example resolution between a global reputation and a local reputation. The local security agent  700  communicates with a server  720  to retrieve global reputation information from the server  720 . The local security agent  700  can receive a communication at  702 . The local security agent can correlate the communication to identify attributes of the message at  704 . The attributes of the message can include, for example, an originating entity, a fingerprint of the message content, a message size, etc. The local security agent  700  includes this information in a query to the server  720 . In other examples, the local security agent  700  can forward the entire message to the server  720 , and the server can perform the correlation and analysis of the message.  
         [0068]     The server  720  uses the information received from the query to determine a global reputation based upon a configuration  725  of the server  720 . The configuration  725  can include a plurality of reputation information, including both information indicating that a queried entity is non-reputable  730  and information indicating that a queried entity is reputable  735 . The configuration  725  can also apply a weighting  740  to each of the aggregated reputations  730 ,  735 . A reputation score determinator  745  can provide the engine for weighting  740  the aggregated reputation information  730 ,  735  and producing a global reputation vector.  
         [0069]     The local security agent  700  then sends a query to a local reputation engine at  706 . The local reputation engine  708  performs a determination of the local reputation and returns a local reputation vector at  710 . The local security agent  700  also receives a response to the reputation query sent to the server  720  in the form of a global reputation vector. The local security agent  700  then mixes the local and global reputation vectors together at  712 . An action is then taken with respect to the received message at  714 .  
         [0070]      FIG. 8  is an example graphical user interface  800  for adjusting the settings of a filter associated with a reputation server. The graphical user interface  800  can allow the user of a local security agent to adjust the settings of a local filter in several different categories  810 , such as, for example, “Virus,” “Worms,” “Trojan Horse,” “Phishing,” “Spyware,” “Spam,” “Content,” and “Bulk.” However, it should be understood that the categories  810  depicted are merely examples, and that the disclosure is not limited to the categories  810  chosen as examples here.  
         [0071]     In some examples, the categories  810  can be divided into two or more types of categories, For example, the categories  810  of  FIG. 8  are divided into a “Security Settings” type  820  of category  810 , and a “Policy Settings” type  830  of category. In each of the categories  810  and types  820 ,  830 , a mixer bar representation  840  can allow the user to adjust the particular filter setting associated with the respective category  810  of communications or entity reputations.  
         [0072]     Moreover, while categories  810  of “Policy Settings” type  830  call be adjusted freely based upon the user&#39;s own judgment, categories of “Security Settings” type  820  can be limited to adjustment within a range. This distinction can be made in order to prevent a user front altering the security settings of the security agent beyond an acceptable range. For example, a disgruntled employee could attempt to lower the security settings, thereby leaving an enterprise network vulnerable to attack. Thus, the ranges  850  placed on categories  810  in the “Security Settings” type  820  are operable to keep security at a minimum level to prevent the network from being compromised. However, as should be noted, the “Policy Settings” type  830  categories  810  are those types of categories  810  that would not compromise the security of a network, but might only inconvenience the user or the enterprise if the settings were lowered.  
         [0073]     Furthermore, it should be recognized that in various examples, range limits  850  can be placed upon all of the categories  810 . Thus, the local security agent would prevent users from setting the mixer bar representation  840  outside of the provided range  850 . It should also be noted, that in some examples, the ranges may not be shown on the graphical user interface  800 . Instead, the range  850  would be abstracted out of the graphical user interface  800  and all of the settings would be relative settings. Thus, the category  810  could display and appear to allow a full range of settings, while transforming the setting into a setting within the provided range. For example, the “Virus” category  810  range  850  is provided in this example as being between level markers 8 and 13. If the graphical user interface  800  were set to abstract the allowable range  850  out of the graphical user interface  800 , the “Virus” category  810  would allow setting of the mixer bar representation  840  anywhere between 0 and 14. However, the graphical user interface  800  could transform the 0-14 setting to a setting within the 8 to 13 range  850 . Thus, if a user requested a setting of midway between 0 and 14, the graphical user interface could transform that setting into a setting of midway between 8 and 13.  
         [0074]      FIG. 9  is a block diagram illustrating reputation based connection throttling for voice over internet protocol (VoIP) or short message service (SMS) communications. As should be understood, an originating IP phone  900  can place a VoIP call to a receiving IP phone  910 . These IP phones  900 ,  910  can be, for example, computers executing soft-phone software, network enabled phones, etc. The originating IP phone  900  can place a VoIP call through a network  920  (e.g., the internet). The receiving IP phone  910  can receive the VoIP call through a local network  930  (e.g., an enterprise network).  
         [0075]     Upon establishing a VoIP call, the originating IP phone has established a connection to the local network  930 . This connection can be exploited similarly to the way e-mail, web, instant messaging, or other internet applications can be exploited for providing unregulated connect to a network. Thus, a connection to a receiving IP phone can be exploited, thereby putting computers  940 ,  950  operating on the local network  930  at risk for intrusion, viruses, trojan horses, worms, and various other types of attacks based upon the established connection. Moreover, because of the time sensitive nature of VoIP communications, these communications are typically not examined to ensure that the connection is not being misused. For example, voice conversations occur in real-time. If a few packets of a voice conversation are delayed, the conversation becomes stilted and difficult to understand. Thus, the contents of the packets typically cannot be examined once a connection is established.  
         [0076]     However, a local security agent  960  can use reputation information received from a reputation engine or server  970  to determine a reputation associated with the originating IP phone. The local security agent  960  can use the reputation of the originating entity to determine whether to allow a connection to the originating entity. Thus, the security agent  960  can prevent connections to non-reputable entities, as indicated by reputations that do not comply with the policy of the local security agent  960 .  
         [0077]     In some examples, the local security agent  960  can include a connection throttling engine operable to control the flow rate of packets being transmitted using the connection established between the originating IP phone  900  and the receiving IP phone  910 . Thus, an originating entities  900  with a non-reputable reputation can be allowed to make a connection to the receiving IP phone  910 . However, the packet throughput will be capped, thereby preventing the originating entity  900  from exploiting the connection to attack the local network  930 . Alternatively, the throttling of the connection can be accomplished by performing a detailed inspection of any packets originating from non-reputable entities. As discussed above, the detailed inspection of all VoIP packets is not efficient. Thus, quality of service (QoS) can be maximized for connections associated with reputable entities, while reducing the QoS associated with connections to non-reputable entities. Standard communication interrogation techniques can be performed on connections associated with non-reputable entities in order to discover whether any of the transmitted packets received from the originating entity comprise a threat to the network  930 . Various interrogation techniques and systems are described in U.S. Pat. No. 6,941,467, No. 7,089,590, No. 7,096,498, and No. 7,124,438 and in U.S. Patent Application Nos. 2006/00015942, 2006/0015563, 9003/0172302, 2003/0172294, 2003/0172291, and 2003/0172166, which are hereby incorporated by reference.  
         [0078]      FIG. 10  is a block diagram illustrating an operation of a reputation based load balancer  1000 . The load balancer  1000  is operable to receive communications from reputable and non-reputable entities  1010 ,  1020  (respectively) through a network  1030  (e.g., the internet). The load balancer  1000  communicates with a reputation engine  1040  to determine the reputation of entities  1010 ,  1020  associated with incoming or outgoing communications.  
         [0079]     The reputation engine  1030  is operable to provide the load balancer with a reputation vector. The reputation vector can indicate the reputation of the entity  1010 ,  1020  associated with the communication in a variety of different categories. For example, the reputation vector might indicate a good reputation for an entity  1010 ,  1020  with respect to the entity  1010 ,  1020  originating spam, while also indicating a poor reputation for the same entity  1010 ,  1020  with respect to that entity  1010 ,  1020  originating viruses.  
         [0080]     The load balancer  1000  can use the reputation vector to determine what action to perform with respect to a communication associated with that entity  1010 ,  1020 . In situations where a reputable entity  1010  is associated with the communication, the message is sent to a message transfer agent (MTA)  1050  and delivered to a recipient  1060 .  
         [0081]     In situations where a non-reputable entity  1020  has a reputation for viruses, but does not have a reputation for other types of non-reputable activity, the communication is forwarded to one of a plurality of virus detectors  1070 . The load balancer  1000  is operable to determine which of the plurality of virus detectors  1070  to use based upon the current capacity of the virus detectors and the reputation of the originating entity. For example, the load balancer  1000  could send the communication to the least utilized virus detector. In other examples, the load balancer  1000  might determine a degree of non-reputability associated with the originating entity and send slightly non-reputable communications to the least utilized virus detectors, while sending highly non-reputable communications to a highly utilized virus detector, thereby throttling the QoS of a connection associated with a highly non-reputable entity.  
         [0082]     Similarly, in situations where a non-reputable entity  1020  has a reputation for originating spam communications, but no other types of non-reputable activities, the load balancer can send the communication to specialized spam detectors  1080  to the exclusion of other types of testing. It should be understood that in situations where a communication is associated with a non-reputable entity  1020  that originates multiple types of non-reputable activity, the communication can be sent to be tested for each of the types of non-reputable activity that the entity  1020  is known to display, while avoiding tests associated with non-reputable activity that the entity  1020  is not known to display.  
         [0083]     In some examples, every communication can receive routine testing for multiple types of non-legitimate content, However, when an entity  1020  associated with the communication shows a reputation for certain types of activity, the communication can also be quarantined for detailed testing for the content that the entity shows a reputation for originating.  
         [0084]     In yet further examples, every communication may receive the same type of testing. However, communications associated with reputable entities  1010  is sent to the testing modules with the shortest queue or to testing modules with spare processing capacity. On the other hand, communications associated with non-reputable entities  1020  is sent to testing modules  1070 ,  1080  with the longest queue. Therefore, communications associated with reputable entities  1010  can receive priority in delivery over communications associated with non-reputable entities. Quality of service is therefore maximized for reputable entities  1010 , while being reduced for non-reputable entities  1020 . Thus, reputation based load balancing can protect the network from exposure to attack by reducing the ability of a non-reputable entity to connect to the network  930 .  
         [0085]      FIG. 11A  is a flowchart illustrating an example operational scenario for collection of geolocation based data for authentication analysis. At step  1100  the operational scenario collects data from various login attempts. Step  1100  can be performed for example by a local security agent, such as the security agent  100  of  FIG. 1 . The collected data can include IP address associated with the login attempt, time of the login attempt, number of login attempts before successful, or the details of any unsuccessful passwords attempted, among many other types of information. The collected data is then analyzed in step  1105  to derive statistical information such as, for example, a geographical location of the login attempts. Step  1105  can be performed, for example, by a reputation engine. The statistical information associated with the login attempts is then stored at step  1110 . The storing can be performed, for example, by a system data store.  
         [0086]      FIG. 11B  is a flowchart illustrating an example operational scenario for geolocation based authentication. A login attempt is received at step  1115 . The login attempt can be received for example, by a secure web server operable to provide secure financial data over a network. It is then determined whether the login attempt matches a stored username and password combination at step  1120 . Step  1120  can be performed, for example, by a secure server operable to authenticate login attempts. If the username and password do not match a stored username/password combination, the login attempt is declared a failure at step  1125 .  
         [0087]     However, if the username and password do match a legitimate username/password combination, the origin of the login attempt is ascertained at step  1130 . The origin of the login attempt can be determined by a local security agent  100  as described in  FIG. 1 . Alternatively, the origin of the login attempt can be determined by a reputation engine. The origin of the login attempt can then be compared with the statistical information derived in  FIG. 11A , as shown in step  1135 . Step  1135  can be performed, for example, by a local security agent  100  or by a reputation engine. It is determined whether the origin matches statistical expectations at step  1140 . If the actual origin matches statistical expectations, the user is authenticated at step  1145 .  
         [0088]     Alternatively, if the actual origin does not match statistical expectations for the origin, further processing is performed in step  1150 . It should be understood that further processing can include requesting further information from the user to verify his or her authenticity. Such information can include, for example, home address, mother&#39;s maiden name, place of birth, or any other piece of information known about the user (e.g., secret question). Other examples of additional processing can include searching previous login attempts to determine whether the location of the current login attempt is truly anomalous or merely coincidental. Furthermore, a reputation associated with the entity originating the login attempt can be derived and used to determine whether to allow the login.  
         [0089]      FIG. 11C  is a flowchart illustrating another example operational scenario for geolocation based authentication using reputation of an originating entity to confirm authentication. A login attempt is received at step  1155 . The login attempt can be received for example, by a secure web server operable to provide secure financial data over a network. It is then determined whether the login attempt matches a stored username and password combination at step  1160 . Step  1160  can be performed, for example, by a secure server operable to authenticate login attempts. If the username and password do not match a stored username/password combination, the login attempt is declared a failure at step  1165 .  
         [0090]     However, if the username and password do match a legitimate username/password combination, the origin of the login attempt is ascertained at step  1170 . The origin of the login attempt can be determined by a local security agent  100  as described in  FIG. 1 . Alternatively, the origin of the login attempt can be determined by a reputation engine. A reputation associated with the entity originating the login attempt can then be retrieved, as shown in step  1175 . Step  1175  can be performed, for example, by a reputation engine. It is determined whether the reputation of the originating entity is reputable at step  1180 . If the originating entity is reputable, the user is authenticated at step  1185 .  
         [0091]     Alternatively, if the originating entity is non-reputable, further processing is performed in step  1190 . It should be understood that further processing can include requesting further information from the user to verify his or her authenticity. Such information can include, for example, home address, mother&#39;s maiden name, place of birth, or any other piece of information known about the user (e.g., secret question). Other examples of additional processing can include searching previous login attempts to determine whether the location of the current login attempt is truly anomalous or merely coincidental.  
         [0092]     Thus, it should be understood that reputation systems can be applied to identifying fraud in financial transactions. The reputation system can raise the risk score of a transaction depending on the reputation of the transaction originator or the data in the actual transaction (source, destination, amount, etc). In such situations, the financial institution can better determine the probability that a particular transaction is fraudulent based upon the reputation of the originating entity.  
         [0093]      FIG. 12  is a flowchart illustrating an example operational scenario for a reputation based dynamic quarantine. Communications are received at step  1200 . The communications are then analyzed to determine whether they are associated with an unknown entity at step  1205 . It should be noted, however, that this operational scenario could be applied to any communications received, not merely communications received from previously unknown entities. For example, communications received from a non-reputable entity could be dynamically quarantined until it is determined that the received communications do no pose a threat to the network. Where the communications are not associated with a new entity, the communications undergo normal processing for incoming communications as shown in step  1210 .  
         [0094]     If the communications are associated with a new entity, a dynamic quarantine counter is initialized in step  1215 . Communications received from the new entity are then sent to a dynamic quarantined at step  1220 . The counter is then checked to determine whether the counter has elapsed in step  1225 . If the counter has not elapsed, the counter is decremented in step  1230 . The behavior of the entity as well as the quarantined communications can be analyzed in step  1235 . A determination is made whether the quarantined communications or behavior of the entity is anomalous in step  1240 . If there is no anomaly found, the operational scenario returns to step  1220 , where new communications are quarantined.  
         [0095]     However, if the communications or behavior of the entity are found to be anomalous in step  1240 , a non-reputable reputation is assigned to the entity in step  1245 . The process ends by sending notification to an administrator or recipients of communications sent by the originating entity.  
         [0096]     Returning to step  1220 , the process of quarantining and examining, communications and entity behavior continues until anomalous behavior is discovered, or until the dynamic quarantine counter elapses in step  1225 . If the dynamic quarantine counter elapses, a reputation is assigned to the entity at step  1255 . Alternatively, in situations where the entity is not an unknown entity, the reputation would be updated in steps  1245  or  1255 . The operational scenario ends at step  1260  by releasing the dynamic quarantine where the dynamic quarantine counter has elapsed without discovery of an anomaly in the communications or in the originating entity behavior.  
         [0097]      FIG. 13  is an example graphical user interface  1300  display of an image spam communication which can be classified as an unwanted image or message. As should be understood, image spam poses a problem for traditional spam filters. Image spam bypasses the traditional textual analysis of spam by converting the text message of the spam into an image format.  FIG. 13  shows an example of image spam. The message shows an image  1310 . While the image  1300  appears to be textual, it is merely the graphic encoding of a textual message. Image spam also typically includes a textual message  1320  comprising sentences which are structured correctly, but make no sense in the context of the message. The message  1320  is designed to elude spam filters that key on communications that only include an image  1310  within the communication. Moreover, the message  1320  is designed to trick filters that apply superficial testing to the text of a communication that includes an image  1310 . Further, while these messages do include information about the origination of the message in the header  1330 , an entity&#39;s reputation for originating image spam might not be known until the entity is caught sending image spam.  
         [0098]      FIG. 14  is a flowchart illustrating an example operational scenario for detecting unwanted images (e.g., image spam). It should be understood that many of the steps shown in  FIG. 14  can be performed alone or in combination with any or all of the other steps shown in  FIG. 14  to provide some detection of image spam. However, the use of each of the steps in  FIG. 14  provides a comprehensive process for detecting image spam.  
         [0099]     The process begins at step  1400  with analysis of the communication. Step  1400  typically includes analyzing the communication to determine whether the communication includes an image that is subject to image spam processing. At step  1410 , the operational scenario performs a structural analysis of the communication to determine whether the image comprises spam. The header of the image is then analyzed in step  1420 . Analysis of the image header allows the-system to determine whether anomalies exist with respect to the image format itself (e.g., protocol errors, corruption, etc.). The features of the image are analyzed in step  1430 . The feature analysis is intended to determine whether any of the features of the image are anomalous.  
         [0100]     The image can be normalized in step  1440 . Normalization of an image typically includes removal of random noise that might be added by a spammer to avoid image fingerprinting techniques. Image normalization is intended to convert the image into a format that can be easily compared among images. A fingerprint analysis can be performed on the normalized image to determine whether the image matches images from previously received known image spam.  
         [0101]      FIG. 15A  is a flowchart illustrating an operational scenario for analyzing the structure of a communication. The operational scenario begins at step  1500  with analysis of the message structure. At step  1505  the hypertext mark-up language (HTML) structure of the communication is analyzed to introduce n-gram tags as additional tokens to a Bayesian analysis. Such processing can analyze the text  1320  that is included in an image spam communication for anomalies. The HTML, structure of the message can be analyzed to define meta-tokens. Meta-tokens are the HTML content of the message, processed to discard any irrelevant HTML tags and compressed by removing white space to create a “token” for Bayesian analysis. Each of tile above described tokens can be used as input to a Bayesian analysis for comparison to previously received communications.  
         [0102]     The operational scenario then includes image detection at step  1515 . The image detection can include partitioning the image into a plurality of pieces and performing fingerprinting on the pieces to determine whether the fingerprints match pieces of previously received images.  
         [0103]      FIG. 15B  is a flowchart illustrating an operational scenario for analyzing the features of an image to extract features of the message for input into a clustering engine to identify components of the image which align with known image spam. The operational scenario begins at step  1520  where a number of high level features of the image are detected for use in a machine learning algorithm. Such features can include values such as the number of unique colors, number of noise black pixels, number of edges in horizontal direction (sharp transitions between shapes), etc.  
         [0104]     One of the features extracted by the operational scenario can include the number of histogram modes of the image, as show at step  1525 . The number of modes is yielded by an examination of spectral intensity of the image. As should be understood, artificial images will typically include fewer modes than natural images, because natural image colors are typically spread through a broad spectrum.  
         [0105]     As described above, the features extracted from the image can be used to identify anomalies. In some examples, anomalies can include analyzing the characteristics of a message to determine a level of similarity of a number of features to the features of stored unwanted images. Alternatively, in some examples, the image features can also be analyzed for comparison with known reputable images to determine similarity to reputable images. It should be understood that none of the extracted features alone are determinative of a classification. For example, a specific feature might be associated with 60% of unwanted messages, while also being associated with 40% of wanted messages. Moreover, as the value associated with the feature changed, there might be a change in the probability that the message is wanted or unwanted. There are many features that can indicate a slight tendency. If each of these features are combined the image spam detection system can make classification decision.  
         [0106]     The aspect ratio is then examined in step  1530  to determine whether there are any anomalies with respect to the image size or aspect. Such anomalies in the aspect ratio could be indicated by similarity of the image size or aspect ratio to known sizes or aspect ratios which are common to known image spam. For example, image spam can come in specific sizes to make the image spam look more like common e-mail. Messages that include images which share a common size with known spam images are more likely to be spam themselves. Alternatively, there are image sizes which are not conducive to spam (e.g., a 1″×1″ square image might be difficult to read if a spammer inserted a message into the image). Messages that include images which are known to be non-conducive to spam insertion are less likely to be image spam. Thus, the aspect ratio of a message can be compared to common aspect ratios used in image spam to determine a probability that the image is an unwanted image or that the image is a reputable image.  
         [0107]     At step  1535 , the frequency distribution of the image is examined. Typically, natural pictures have uniform frequency distribution with a relative scarcity of sharp frequency gradations. On the other hand, image spam typically includes a choppy frequency distribution as a result of black letters being placed on a dark: background. Thus, such non-uniform frequency distribution can indicate image spam.  
         [0108]     At step  1540 , the signal to noise ratio can be analyzed. A high signal to noise ratio might indicate that a spammer may be trying to evade fingerprinting techniques by introducing noise into the image. Increasing noise levels can thereby indicate an increasing probability that the image is an unwanted image.  
         [0109]     It should be understood that some features can be extracted on the scale of the entire image, while other features can be extracted from subparts of the image. For example, the image can be subdivided into a plurality of subparts. Each of the rectangles can be transformed into a frequency domain using a fast Fourier transform (FFT). In the transformed image, the predominance of frequencies in a plurality of directions can be extracted as features. These subparts of the transformed image can also be examined to determine the amount of high frequencies and low frequencies. In the transformed image, the points that are further away from the origin represent higher frequencies. Similarly to the other extracted features, these features can then be compared to known legitimate and unwanted images to determine which characteristics the unknown image shares with each type of known image. Moreover, the transformed (e.g., frequency domain) image can also be divided into subparts (e.g., slices, rectangles, concentric circles, etc.) and compared against data from known images (e.g., both known unwanted images and known legitimate images).  
         [0110]      FIG. 15C  is a flowchart illustrating an operational scenario for normalizing the an image for spam processing. At step  1545 , obfuscation and noise is removed from the image. As discussed previously, these can be introduced by spammers to evade fingerprinting techniques such as hashing by varying the sum of the hash such that it does not match any previously received hash fingerprints of known image spam. Obfuscation and noise removal can describe several techniques for removing artificial noise introduced by spammers. It should be understood that artificial noise can include techniques used by spammers such as banding (where a font included in the image is varied to vary the hash of the image).  
         [0111]     An edge detection algorithm can be run on the normalized image at step  1550 . In some examples, the edge detected image can be used provided to an optical character recognition engine to convert the edge detected image to text. The edge detection can be used to remove unnecessary detail from the picture which can cause inefficiency in processing the image again other images.  
         [0112]     At step  1555 , median filtering can be applied. The median filtering is applied to remove random pixel noise. Such random pixels can cause problems to content analysis of the image. The median filtering can help to remove single pixel type of noise introduced by spammers. It should be understood that single pixel noise is introduced by spammers using an image editor to alter one or more pixels in the image, which can make the image appear grainy in some areas, thereby making the image more difficult to detect.  
         [0113]     At step  1560 , the image is quantized. Quantizing of the image remove unnecessary color information. The color information typically requires more processing and is unrelated to the attempted propagation of the spam. Moreover, spammers could vary the color scheme in an image slightly and again vary the hash such that known image spam hashes would not match the derived hash from the color variant image spam.  
         [0114]     At step  1565 , contrast stretching is performed. Using contrast stretching the color scale in the image is maximized from black to white, even if the colors only vary through shades of gray. The lightest shade of the image is assigned a white value, while the darkest shade in the image is assigned a black value. All other shades are assigned their relative position in the spectrum in comparison to the lightest and darkest shades in the original image. Contrast stretching helps to define details in an image that may not make full use of the available spectrum and therefore can help to prevent spammers from using different pieces of the spectrum to avoid fingerprinting techniques. Spammers sometimes intentionally shift the intensity range of an image to defeat some types of feature identification engines. Contrast stretching can also help normalize an image such that it can be compared to other images to identify common features contained in the images.  
         [0115]      FIG. 15D  is a flowchart illustrating an operational scenario for analyzing the fingerprint of an image to find common fragments among multiple images. The operational scenario begins a step  1570  by defining regions within an image. A winnowing algorithm is then performed on the defined regions to identify the relevant portions of the image upon which fingerprints should be taken at step  1575 . At step  1580 , the operational scenario fingerprints the resulting fragments from the winnowing operation and determines whether there is a match between the fingerprints of the received image an known spam images. A similar winnowing fingerprint approach is described in U.S. Patent Application Publication No. 2006/025068, which is hereby incorporated by reference.  
         [0116]     As used in the description herein and throughout the claims that follow, the meaning of “a,” “an,” and “the” includes plural reference unless the context clearly dictates otherwise. Also, as used in the description herein and throughout the claims that follow, the meaning of “in” includes “in” and “on” unless the context clearly dictates otherwise. Finally, as used in the description herein and throughout the claims that follow, the meanings of “and” and “or” include both the conjunctive and disjunctive and may be used interchangeably unless the context clearly dictates otherwise.  
         [0117]     Ranges may be expressed herein as from “about” one particular value, and/or to “about” another particular value. When such a range is expressed, another embodiment includes from the one particular value and/or to the other particular value. Similarly, when values are expressed as approximations, by use of the antecedent “about,” it will be understood that the particular value forms another embodiment. It will be further understood that the endpoints of each of the ranges are significant both in relation to the other endpoint, and independently of the other endpoint.  
         [0118]     A number of embodiments of the invention have been described. Nevertheless, it will be understood that various modifications may be made without departing from the spirit and scope of the invention. Accordingly, other embodiments are within the scope of the following, claims.