Online fraud detection dynamic scoring aggregation systems and methods

In some embodiments, an online fraud prevention system combines the output of several distinct fraud filters, to produce an aggregate score indicative of the likelihood that a surveyed target document (e.g. webpage, email) is fraudulent. Newly implemented fraud filters can be incorporated and ageing fraud filters can be phased out without the need to recalculate individual scores or to renormalize the aggregate fraud score. Every time the output of an individual filter is calculated, the aggregate score is updated in a manner which ensures the aggregate score remains within predetermined bounds defined by a minimum allowable score and a maximum allowable score (e.g., 0 to 100).

BACKGROUND

The invention relates to methods and systems for detecting online fraud.

Online fraud, especially in the form of phishing and identity theft, has been posing an increasing threat to Internet users worldwide. Sensitive identity information such as user names, IDs, passwords, social security and medical records, bank and credit card details obtained fraudulently by international criminal networks operating on the Internet are used to withdraw private funds and/or are further sold to third parties. Beside direct financial damage to individuals, online fraud also causes a range on unwanted side effects, such as increased security costs for companies, higher retail prices and banking fees, declining stock values, lower wages and decreased tax revenue.

In an exemplary phishing attempt, a fake website, sometimes also termed a clone, may pose as a genuine webpage belonging to an online retailer or a financial institution, asking the user to enter some personal/account information (e.g., username, password) and/or financial information (e.g. credit card number, account number, card security code). Once the information is submitted by the unsuspecting user, it is harvested by the fake website. Additionally, the user may be directed to another webpage which may install malicious software on the user's computer. The malicious software (e.g., viruses, Trojans) may continue to steal personal information by recording the keys pressed by the user while visiting certain webpages, and may transform the user's computer into a platform for launching other phishing or spam attacks.

Software running on an Internet user's computer system may be used to identify fraudulent online documents and to warn the user of a possible phishing/identity theft threat. Several approaches have been proposed for identifying a clone webpage, such as matching the webpage's Internet address to lists of known phishing or trusted addresses (techniques termed black- and white-listing, respectively).

In U.S. Pat. No. 7,457,823 B2, Shraim et al. describe a system which performs a plurality of tests on a web site or an electronic communication, assigns a score based on each of the tests, assigns a composite score based on the scores for each of the plurality of tests, and categorizes the web site/electronic communication as legitimate or fraudulent according to the plurality of scores and/or the composite score.

Experienced fraudsters are continuously developing countermeasures to such detection tools. Such countermeasures include frequently changing the IP addresses of the clone pages to escape blacklisting. Since the type and methods of online fraud evolve rapidly, successful detection may benefit from the development of new fraud-identifying tests.

SUMMARY

According to one aspect, a method comprises employing a computer system to determine an aggregate fraud score of a target document as a combination of a first fraud score and a second fraud score of the target document, wherein the first and second fraud scores are determined according to distinct fraud-evaluation procedures; determining a third fraud score of the target document; in response to determining the third fraud score, modifying the aggregate fraud score by a first amount determined according to a product of the third fraud score and a difference between the aggregate score and a maximum allowable aggregate score; and, in response to modifying the aggregate fraud score, determining whether the target document is fraudulent according to the modified aggregate score.

According to another aspect, a computer system comprises at least one processor programmed to: determine an aggregate fraud score of a target document as a combination of a first fraud score and a second fraud score of the target document, wherein the first and second fraud scores are determined according to distinct fraud-evaluation procedures; determine a third fraud score of the target document; in response to determining the third fraud score, modify the aggregate fraud score by a first amount determined according to a product of the third fraud score and a difference between the aggregate score and a maximum allowable aggregate score; and, in response to modifying the aggregate fraud score, determine whether the target document is fraudulent according to the modified aggregate score.

According to another aspect, a method comprises employing a computer system to determine whether a target document comprises a fraud-indicative feature; in response to determining whether the target document comprises the target-indicative feature, when the target document comprises the fraud-indicative feature, employing the computer system to modify a current value of an aggregate fraud score for the target document by an amount proportional to a difference between the current value of the aggregate score and a maximum allowable value of the aggregate fraud score, wherein the aggregate score is determined as a combination of a plurality of individual fraud scores; and in response to modifying the current value of the aggregate fraud score, employing the computer system to determine whether the electronic document is fraudulent according to the modified current value of the aggregate fraud score.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

In the following description, it is understood that all recited connections between structures can be direct operative connections or indirect operative connections through intermediary structures. A set of elements includes one or more elements. Any recitation of an element is understood to refer to at least one element. A plurality of elements includes at least two elements. Unless otherwise required, any described method steps need not be necessarily performed in a particular illustrated order. A first element (e.g. data) derived from a second element encompasses a first element equal to the second element, as well as a first element generated by processing the second element and optionally other data. Making a determination or decision according to a parameter encompasses making the determination or decision according to the parameter and optionally according to other data. Unless otherwise specified, an indicator of some quantity/data may be the quantity/data itself, or an indicator different from the quantity/data itself. Computer programs described in some embodiments of the present invention may be stand-alone software entities or sub-entities (e.g., subroutines, code objects) of other computer programs. Unless otherwise specified, the term online fraud is not limited to fraudulent websites, but also encompasses other non-legitimate or unsolicited commercial electronic communications such as email, instant messages, and phone text and multimedia messages, among others. Computer readable media encompass non-transitory storage media such as magnetic, optic, and semiconductor media (e.g. hard drives, optical disks, flash memory, DRAM), as well as communications links such as conductive cables and fiber optic links. According to some embodiments, the present invention provides, inter alia, computer systems comprising hardware (e.g. one or more processors and/or memory) programmed to perform the methods described herein, as well as computer-readable media encoding instructions to perform the methods described herein.

The following description illustrates embodiments of the invention by way of example and not necessarily by way of limitation.

FIG. 1shows an exemplary online fraud prevention system according to some embodiments of the present invention. System10includes a plurality of web servers12a-b, an anti-fraud server16, and a plurality of client systems14a-b. Client systems14a-bmay represent end-user computers, each having a processor, memory, and storage, and running an operating system such as Windows®, MacOS® or Linux. Some client computer systems14a-bmay represent mobile computing and/or telecommunication devices such as tablet PCs, mobile telephones, and personal digital assistants (PDA). In some embodiments, client systems14a-bmay represent individual customers, or several client systems may belong to the same customer. Anti-fraud server16may include one or more computer systems. A network18connects web servers12a-b, client systems14a-b, and anti-fraud server16. Network18may be a wide-area network such as the Internet, while parts of network18may also include a local area network (LAN).

FIG. 2shows an exemplary hardware configuration of a client system14. In some embodiments, system14comprises a processor20, a memory unit22, a set of input devices24, a set of output devices28, a set of storage devices26, and a communication interface controller30, all connected by a set of buses32.

In some embodiments, processor20comprises a physical device (e.g. multi-core integrated circuit) configured to execute computational and/or logical operations with a set of signals and/or data. In some embodiments, such logical operations are delivered to processor20in the form of a sequence of processor instructions (e.g. machine code or other type of software). Memory unit22may comprise volatile computer-readable media (e.g. RAM) storing data/signals accessed or generated by processor20in the course of carrying out instructions. Input devices24may include computer keyboards and mice, among others, allowing a user to introduce data and/or instructions into system14. Output devices28may include display devices such as monitors. In some embodiments, input devices24and output devices28may share a common piece of hardware, as in the case of touch-screen devices. Storage devices26include computer-readable media enabling the non-volatile storage, reading, and writing of software instructions and/or data. Exemplary storage devices26include magnetic and optical disks and flash memory devices, as well as removable media such as CD and/or DVD disks and drives. Communication interface controller30enables system14to connect to network18and/or to other machines/computer systems. Typical communication interface controllers30include network adapters. Buses32collectively represent the plurality of system, peripheral, and chipset buses, and/or all other circuitry enabling the inter-communication of devices20-30of system14. For example, buses32may comprise the northbridge bus connecting processor20to memory22, and/or the southbridge bus connecting processor20to devices24-30, among others.

FIG. 3shows an exemplary hardware configuration of anti-fraud server16, according to some embodiments of the present invention. Anti-fraud server16may be a computer system comprising a server processor120, a server memory122, a set of server storage devices126, and a server communication interface controller130, all connected to each other via a set of server buses132. Although some details of hardware configuration may differ between anti-fraud server16and client system14(FIG. 2), the scope of devices120,122,126,130and132may be similar to that of devices20,22,26,30, and32described above, respectively.

FIG. 4shows an exemplary set of applications executing on a client system14. In some embodiments, each client system14a-bcomprises a document reader application34(e.g. web browser, email reader, media player), which may be a computer program used to remotely access data stored on web servers12a-b. When a user accesses an online document such as a webpage or electronic message (termed target document in the following discussion), data associated to the target document circulates on parts of network18between the respective web server and client system14. In some embodiments, document reader application34receives the target document data, translates it into visual form and displays it to the user, allowing the user to interact with the target document's content.

In some embodiments, document reader application34includes a client fraud detector36and a client communication manager37connected to document reader34. In some embodiments, client fraud detector36may determine whether a target document is fraudulent. For example, if a target webpage replicates the visual/semantic characteristics of a legitimate bank webpage requesting the credentials of the user, client fraud detector36may identify the target webpage as a phishing page. If fraud is detected, some embodiments of detector36may block the display of the target webpage by document reader34and/or issue a fraud warning to the user. Fraud detector36may be integrated with document reader34in the form of a plug-in, add-on, or toolbar. Alternatively, client fraud detector36may be a stand-alone software application, or may be a module of a security suite having antivirus, firewall, anti-spam, and other modules. In some embodiments, the operation of fraud detector36may be turned on and off by a user.

In some embodiments, client communication manager37is configured to manage communication of client system14with anti-fraud server16and/or webservers12a-b. For example, manager37may establish connections over network18, and send and receive data to/from servers12a-band16.

FIG. 5shows a set of exemplary applications executing on anti-fraud server16according to some embodiments of the present invention. Anti-fraud server16may comprise a server fraud detector38, a server communication manager46, a fraud score database42and a filter parameter database44, all connected to detector38. In some embodiments, server16may also comprise a filter training engine48connected to filter parameter database44. In some embodiments, server fraud detector38is configured to perform a plurality of fraud detection transactions with client systems14a-b. For each such transaction, server fraud detector38is configured to conduct a server-side scan to determine whether a target document accessed by the respective client system is fraudulent or not, as described in detail below. Server communication manager46is configured to manage communication with client systems14a-b. For example, manager46may establish connections over network18, send and receive data to/from client systems14a-b, maintain a list of ongoing fraud detection transactions, and associate target document data with originating client systems14a-b.

Fraud score database42is maintained as a repository of online fraud knowledge. In some embodiments, database42comprises a plurality of recorded fraud scores calculated for a plurality of target documents, as described further below. Each score stored in database42may include additional information, such as a time stamp indicating a point in time when the respective score was calculated or updated, and/or an indicator (e.g. filter ID) of the fraud filter employed to compute the respective score (see below). Along with fraud scores, database42may also store a data structure comprising a plurality of target object identifiers (e.g. object IDs, tags, hashes), each object identifier uniquely associated to a target document, and a mapping associating each fraud score with the target document it was calculated for, allowing server fraud detector38to selectively retrieve recorded fraud scores from database42, as shown below. In some embodiments, fraud score database42may reside on a computer system distinct from server16, but connected to server16via network18. Alternatively, database42may reside on non-volatile computer-readable media connected to server16.

In some embodiments, filter parameter database44comprises a set of filter-specific parameters determining the operation of fraud filters (see below). Examples of filter parameters include a number of neurons per layer and a set of neuronal weights of a neural network-based filter, the position of cluster centers in a k-means-based classifier, and the number and position of color histogram bins in an image-processing filter. Other examples of filter parameters include a decision threshold, a set of network addresses, a set of fraud-indicative keywords, and a blacklist/whitelist of domain names. In some embodiments, the values of filter parameters stored in database44are provided by human operators. In some embodiments, fraud filters may be trained (optimized) to improve fraud-detection performance by varying the values of filter parameters. For example, filter training engine48may be configured to produce a set of filter parameters (e.g., training a neural network filter to distinguish fraudulent from legitimate documents may produce a set of neuronal weights) to be stored in database44. In some embodiments, filter training engine48may operate on a computer system distinct from anti-fraud server16, in which case filter parameters computed by engine48may be transferred to server16via periodic or on-demand updates.

FIG. 6illustrates an exemplary client-server fraud detection transaction. When a user requests to access an online document (e.g. a webpage), the respective client system14may send a target indicator40to anti-fraud server16, and may receive a target label50from server16. In some embodiments, target indicator40comprises data allowing anti-fraud server16to selectively access and/or retrieve the respective target document. Exemplary target indicators40comprise a uniform resource locator (URL) of a target webpage, a network address of a target document, and an IP address of a target Internet domain. In some embodiments, target indicator40may comprise an object identifier (e.g. a hash) of the target object, an address (e.g. a pointer) of the target object in a database accessible to server16, or the target object itself, in part or in its entirety. Some embodiments of target indicator40may also comprise other data associated to the respective target document (e.g. a field from the HTTP header of the target document, a size and/or timestamp of the target document).

In some embodiments, target label50comprises an indicator of a fraud status (e.g. fraudulent, legitimate) of the target document, determined by anti-fraud server16in the course of the respective fraud detection transaction. Target label50may also comprise an identifier (object ID, etc.) of the respective target object, as well as other data such as a timestamp and an indicator of the type of fraud detected (e.g., phishing).

FIG. 7shows a diagram of server fraud detector38according to some embodiments of the present invention. Fraud detector38comprises a parser52, a set of fraud filters54(denoted F1 . . . Fn inFIG. 7) connected to parser52, a score aggregator70connected to filters54, and a decision module66connected to score aggregator70. In some embodiments, fraud detector38receives target indicator40from client system14and produces target label50indicating whether the target document identified by indicator40is fraudulent or not. Server fraud detector38may also retrieve a recorded fraud score62from fraud score database42and a set of filter parameters56from filter parameter database44, and may output an aggregate fraud score64to score database42.

In some embodiments, parser52receives target indicator40and processes the target document associated with indicator40into a form which is suitable as input for the various fraud filters54. For example, when the target document is a webpage, parser52may break up the target webpage into constituent entities (e.g. header, body, text parts, images, etc.), may identify various features such as forms and hyperlinks, and extract specific data from the HTTP header (e.g. the referrer URL), among others. In some embodiments, parser52may determine a location of the target document (e.g., a URL) according to target indicator40, and instruct server communication manager46to download a copy of the target document from the respective location.

In some embodiments, fraud filters54are computer programs, each implementing a distinct procedure for evaluating the legitimacy of the document indicated by target indicator40. In some embodiments, operation of each fraud filter54may comprise evaluating the respective target document for fraud-indicative features (characteristic of fraudulent documents) and/or legitimacy-indicative features (characteristic of legitimate documents). An example of a fraud-indicative feature is a fraudulent referrer: when the user is directed to a particular webpage by clicking a link found in a phishing email, the respective webpage has a high probability of being fraudulent. Another fraud-indicative feature is the presence of a login form in a target webpage. An example of legitimacy-indicative feature is high traffic: domains receiving high traffic are less likely to be fraudulent than domains receiving only a few visitors.

A few exemplary fraud filters54are listed below:

a) A referrer filter may determine whether a target document is fraudulent according to a referrer of the respective document. In some embodiments, a referrer is a document (e.g. webpage) which links to and/or directs a user to the target document. For example, the HTTP header of a webpage may comprise the URL of the page visited just before the current one (also known as the referrer URL). In some embodiments, filter54maintains a blacklist and/or whitelist of referrer URLs, and compares the referrer of the target document to the black/whitelist entries. In some embodiments, a page referred to by a blacklisted URL is marked as fraudulent. In other embodiments, referrers recognized as spam messages, malware, and/or social network sites may be associated to higher probability of fraud than referrers such as, e.g., personal webpages and search engines.

b) A page layout filter may determine whether a target document is fraudulent according to the visual layout of the target document. In some embodiments, a webpage visually organized as a login page may be assigned a high probability of being fraudulent.

c) A keyword filter may maintain a list of keywords commonly associated with fraud. The presence of such keywords in a target document may determine the filter to label the respective target document as fraudulent.

d) An Internet domain history filter may use historical data about an Internet domain to determine the legitimacy of a target document hosted by the domain. In some embodiments, when there is indication that the respective domain has ever hosted a fraudulent webpage (e.g. phishing), or has ever been hacked into, the target document may be assigned a high probability of being fraudulent.

e) An Internet domain reputation filter may employ a set of reputation indicators such as an identity and/or address of the domain owner, a date when the domain was first registered under the current ownership, etc. In some embodiments, domains having the same owners as known fraudulent domains may be assigned a high probability of fraud. In some embodiments, domains showing frequent changes of ownership are also assigned a high probability of hosting fraudulent documents.

As the form and content of online fraud are continually changing, the fraud-detecting performance of filters54may vary in time. In some embodiments, the plurality of fraud filters54may be kept up to date by the addition of new filters and removal of older ones considered obsolete. A new filter may be introduced, for example, with the identification of a novel fraud-indicative feature. In some embodiments, fraud filters54may be selectively turned on or off by an operator. Alternatively, filters may be automatically inactivated after a certain time in service (e.g., one year), or according to other criteria. In some embodiments, each fraud filter54may comprise an identifier (filter ID), which distinguishes it from other fraud filters, allowing server fraud detector38to selectively employ any combination of fraud filters, and to maintain a record of which fraud filters were used to evaluate each target document.

Each fraud filter54inputs a set of data of the target document from parser52, and a set of filter parameters56from filter parameter database44, and outputs a score60to score aggregator70. In some embodiments, each score60is a number between 0 and 1. Scores60may be fraud-indicative (high scores denoting a high probability that the target document is fraudulent) and/or legitimacy-indicative (high scores denoting a high probability that the target document is legitimate). For example, a fraud-indicative score of 0.85 produced by a certain fraud filter54may indicate that the respective document has an 85% likelihood of being fraudulent according to that particular fraud filter. In some embodiments, scores60may have binary values (e.g., 1/0, yes/no).

Table 1 shows an exemplary set of scores60produced by a fraud filter54according to estimated Internet traffic. The filter registers a number of requests (queries) from various client systems14to scan a particular target webpage. The number of queries may be indicative of the Internet traffic at the respective URL, and high traffic may be an indication of a legitimate webpage. The exemplary score is legitimacy-identifying (higher score indicative of higher likelihood of legitimacy).

Score aggregator70(FIG. 7) is configured to combine individual scores60produced by fraud filters54into an aggregate score64of the respective target document. In some embodiments, aggregate score64is a number indicative of the likelihood that the target object is fraudulent (e.g., a number between 0 and 100, with 0 indicating a certainty of legitimacy, and 100 indicating a certainty of fraud). In some embodiments, server fraud detector38is configured so that every time a target document is evaluated, a copy of aggregate score64is recorded in score database42, along with an indicator of the target document and an indicator of the fraud filters used in the calculation (e.g., the respective filter IDs). This allows database42to operate like a cache: when the same target document is evaluated again, server fraud detector38may retrieve a recorded score62of the target document from database42, without having to re-compute it, thus conserving computing resources. Only fraud filters54which have not been used previously to analyze the respective target document (e.g. new filters introduced since the last scan of the target document) are employed to produce scores60, which are combined with recorded score62to produce aggregate score64.

To compute aggregate score64, aggregator70may first initialize score64to a value equal to recorded score62of the respective target document. Then, for each fraud filter i producing a score σi, some embodiments of aggregator70may modify aggregate score64iteratively, as follows.

When score σiis fraud-indicative (high score indicative of high likelihood of fraud), the current value of the aggregate score is replaced by a new value:
SA→SA+(Smax−SA)wiσi[1]
wherein SAdenotes the aggregate score, Smaxdenotes an upper bound of the aggregate score (maximum allowable score, e.g., 100), and widenotes a weight of the respective fraud filter. When fraud score σiis legitimacy-indicative (high score indicative of high likelihood of legitimacy), the aggregate score is updated to:
SA→SA−(SA−Smin)wiσi[2]
wherein SAdenotes the aggregate score, Smindenotes a lower bound of the aggregate score (minimum allowable score, e.g., 0), and widenotes a weight of the respective fraud filter.

In some embodiments, each filter weight wiis a number between 0 and 1, representing a degree of reliability of the respective filter. Some features of a target document may associate more strongly with fraud than others. For example, a link to a known phishing page is typically a stronger indication of fraud than the presence of the word “Password”. Consequently, a score σicomputed by a fraud filter specialized in analyzing the hyperlinks of a target document may receive a higher weight withan a score σjcomputed by a fraud filter which detects the presence of keywords such as “Password”. In some embodiments, filter weights wimay be provided by an operator, or may be the result of an automated filter training procedure.

In an exemplary calculation employing formulae [1]-[2], a target webpage received an aggregate score of 40 (measured on a scale from 0 to 100) in a previous fraud scan. At a later time, a reliable new filter is introduced (w1=1); it returns a fraud-indicative score σ1=0.3 for the target webpage. Aggregator70computes a new aggregate score 40+(100-40)*0.3=58. Meanwhile, a domain traffic filter (weight w2=0.5) returns a legitimacy-indicative score σ2=0.2. The aggregate score is now 58−58*0.5*0.2≈52.

In some embodiments, decision module66(FIG. 7) receives aggregate score64from aggregator70and outputs target label50. To determine target label50, some embodiments of decision module66may compare aggregate score64to a predetermined threshold. When score64exceeds the threshold, the target document may be labeled as fraudulent, otherwise it may be labeled as legitimate. An exemplary threshold value of 50 was used in some computer experiments.

FIG. 8shows an exemplary sequence of steps executed by client system14in the course of a fraud detection transaction, according to some embodiments of the present invention. In a step202, system14receives a user request to access a target document (e.g., to display a webpage in a browser application). In a step204, client fraud detector36may determine target indicator40associated to the target document. In the example of the target webpage, indicator40may comprise the URL of the target webpage, among others. In a step206, client communication manager37may establish a connection with anti-fraud server16over network18, to transmit target indicator to server16. Next, in a step208, communication manager37receives target label50from server16. In a step210, fraud detector36determines according to target label50whether the respective target document is fraudulent or not. When label50indicates a legitimate document, in a step212, client system14may load the target document (e.g., display the target webpage to the user). When target label50indicates a fraudulent document, in a step214, client system14may notify the user by e.g. displaying a fraud warning. In some embodiments, step214may further comprise blocking access to the target document.

FIG. 9shows an exemplary sequence of steps performed by anti-fraud server16in the course of a fraud detection transaction, according to some embodiments of the present invention. In a step222, server communication manager46receives target indicator40from client system14. In a step224, server fraud detector38may retrieve recorded score62associated to the respective target document from score database42. Next, in a step226, detector38determines according to the data (e.g. filter IDs) stored in relation to recorded score62which fraud filters54were used to compute score62, and whether a score update is necessary. In some embodiments, a new aggregate score is computed whenever there exists at least one fraud filter54which has not been applied to the target document (for example, every time a new fraud filter is introduced, or when the parameters of an existing fraud filter have changed). When a score update is not required (e.g. when recorded score62is an aggregation of scores60from all filters54), the operation of server16proceeds to a step234described further below. Otherwise, in a step228, parser52may produce a set of data of the target document, suitable as input to filters54. In some embodiments, step228may further comprise remotely accessing or downloading the target document, in part or in its entirety, onto server16.

In a step230, a subset of filters54may input target document data from parser52, to produce corresponding scores60. In a step232, score aggregator70may compute aggregate score64by combining scores60computed in step230with recorded score62retrieved in step224. In some embodiments, aggregator70may employ formula [1] to compute aggregate score64. Next, in a step234, decision module66may produce target label50according to the aggregate score. In some embodiments, when no new score aggregation was carried out, module66may determine target label50according to recorded score62. In a step236, server fraud detector38instructs communication manager46to send target label50to the originating client system14. In a step238, server fraud detector38may update score database42, by replacing recorded score62with the newly computed aggregate score64. In some embodiments, data about the update (e.g., IDs of filters participating in the aggregate score, timestamp, etc.) is saved along with aggregate score64.

The exemplary systems and methods described above allow an online fraud prevention system to employ several distinct fraud filters simultaneously and to dynamically combine the individual outputs of the fraud filters to produce an aggregate score indicative of the likelihood that a surveyed target document (e.g. webpage, electronic communication) is fraudulent.

Online fraud may come in many different forms. Some examples of fraudulent online documents include: a webpage pretending to represent a financial institution; a webpage hosting an escrow scam; a social networking (e.g., Facebook®) page carrying out a scam; a webpage hosting an online casino scam, a money loan scam, or a pay-per-click scam; a webpage hosting an online dating scam or an employment/recruitment scam. Other examples of online fraud are phishing webpages and/or electronic messages attempting to acquire sensitive information such as user names, passwords and credit card details by masquerading as a trustworthy entity. Other fraudulent webpages and electronic messages may contain and/or attempt to install malicious software on a user's computer, said malware being used to steal identity or other private information.

Individual fraud filters evaluate a number of fraud-indicative and/or legitimacy indicative features of the target document, such as determine whether a webpage comprises a login form or a set of fraud-indicative keywords, or whether the Internet domain hosting the target document has a history of hosting fraudulent documents.

In some embodiments, fraud scores produced by individual filters may be fraud-indicative (high score indicative of high likelihood of fraud), or legitimacy-indicative (high score indicative of high likelihood of legitimacy). Fraud-indicative scores may increase the aggregate fraud score, whereas legitimacy-indicative scores may decrease the aggregate score, according to a common calculation procedure.

The exemplary systems and methods described here allow the dynamic incorporation of newly implemented fraud filters and/or the phasing out of ageing fraud filters, without the need to recalculate individual scores produced by said filters, or to renormalize the aggregate fraud score. Every time an individual fraud score is calculated, the aggregate score is updated in a manner which allows it to remain within predetermined bounds (e.g., 0 to 100).

Some embodiments of the present invention conduct a collaborative client-server fraud detection transaction, and assess the fraud status (e.g., fraudulent/legitimate) of the target object according to the results of the server-side scan of the target object. Conducting a part of the fraud detection on a remote server has a number of advantages over local fraud detection on a client computer system.

By performing a significant part of fraud-detection centrally on a server, the systems and methods described above allow for the timely incorporation of data on newly detected online fraud. For example, webpage white/blacklists can be maintained much more efficiently on a central server. By contrast, when fraud detection is performed on client computer systems, updated white/blacklists must be distributed to a great number of clients every time a new threat is discovered.

The size of data packets exchanged between client and anti-fraud server systems described above is kept to a minimum. Instead of sending entire target documents from the client to the server for fraud-detection, the exemplary methods and systems described above are configured to exchange target indicators such as target URL's, amounting to several bytes per target object, thus significantly reducing network traffic.

It will be clear to one skilled in the art that the above embodiments may be altered in many ways without departing from the scope of the invention. Accordingly, the scope of the invention should be determined by the following claims and their legal equivalents.