Symantec Corporation

Verisign, Inc.

Eyal S Lanxner

Shay Raz

Karthik Shyamsunder

Andrew Simpson

Srinivas Sunkara

Alexander Holmes

Leonard Orentas

Mark Kosters

Matt Larson

Tamir Kamara

Oren Gross

Nir Zohar

Shevek Mankin

Yona Benjamin Mankin

A method of categorizing a recent transaction as anomalous includes a) receiving information about a recent transaction and b) accessing information about one or more historical transactions. The one or more historical transactions have at least one party in common with the recent transaction. The method also includes c) determining a similarity value between the recent transaction and a transaction i of the one or more historical transactions and d) determining if the similarity value is greater than or equal to a predetermined threshold value. The method further includes e) if the similarity is greater than or equal to the predetermined threshold value, categorizing the recent transaction as not anomalous or f) if the similarity is less than the predetermined threshold value, determining if there are additional transactions. If there are additional transactions, incrementing counter i and repeating steps c) through f).

Method and system for performing fraud detection for users with infrequent activity

Systems and methods for scoring a domain including analyzing counter data and information obtained from a web site associated with the domain. Methods may include receiving requests to resolve the domain at an authoritative domain resolution server. A counter may be incremented for the domain based on the received requests. Information may be obtained from a web page associated with the domain. For example, obtaining information from the web page may include obtaining quantitative, qualitative, and/or functional information from the web page, such as link information, a status of network links corresponding to the link information, and associated ratios. The status of link information may include searching for functional details and/or results, such as, domain redirections, domain errors, mirror content, and commonly linked sites. A score may be calculated for the domain based upon the counter data and the information obtained from the web page associated with the domain.

Domain popularity scoring

A method of determining a clustering metric includes receiving a first set of transactions and a second set of transactions. For transaction i of the first set and transaction j of the second set, the method includes (a) determining an intersection set, (b) determining a union set; (c) computing a common linkage between transaction i and transaction j equal to the intersection set divided by the union set, and (d) incrementing index j and repeating steps (a)-(c). The method also includes (e) summing the common linkages between transaction i and the transactions of the second set, (f) normalizing the sum of the common linkages by a number of the second set, and (g) incrementing index i and repeating steps (a)-(f). The method further includes (h) summing the normalized common linkages and (i) normalizing the sum of the normalized common linkages by a number of the first set.

Method and system for clustering transactions in a fraud detection system

Systems and methods for scoring a domain web traffic based on DNS traffic requests received at an authoritative name server to resolve the domain name. A request to resolve the domain name is received at an authoritative name server. A counter, such as a server counter or a hit counter, for the domain name is incremented based on the received request. A score, such as a domain traffic score or a domain rank, is calculated based upon a count of the counter. Calculating the score may also include applying a weighting factor to the counters based on information about a requesting set of resolvers and other domains/websites that may be linking and driving traffic to the domain whose traffic score is being calculated. Examples of relevant set of resolvers information may include location, traffic levels, traffic type and architecture of the set of resolvers.

Existent domain name DNS traffic capture and analysis

A method of computing a similarity between a first transaction having a set of properties and a second transaction having the set of properties includes computing an initial weight for each of the properties of the set of properties and computing a similarity between each of the properties of the first transaction and the properties of the second transaction. The method also includes adjusting the initial weight for each of the properties based on a measure of the commonness of each of the properties of the set of properties, normalizing the adjusted weights, and computing the similarity by summing the products of the normalized adjusted weights and the computed similarities.

Method and system for weighting transactions in a fraud detection system

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Modiin, Israel

Eyal S Lanxner