Bitdefender Ipr Management Ltd.

Catalin A Cosoi

Claudiu C N Musat

In some embodiments, a phishing detection method includes computing a first phishing indicator of a target webpage; when the target webpage is considered suspicious of phishing according to the first phishing indicator, computing a second phishing indicator of the target webpage, and deciding whether the webpage is a phishing site according to the first and second phishing indicators. Computing the second phishing indicator comprises comparing a word content (semantic content) of the target webpage to a word content of each of a plurality of reference webpages. Comparing the word contents may include counting the number of visible words which are common to the target and reference webpages, and/or computing a ratio of a number of words which are common to the target and reference webpages to the total number of words in both the target and reference webpages.

Systems and methods for electronic fraud prevention

In some embodiments, a spam filtering method includes computing the relevance of each of a plurality of anti-spam filters according to a relevance parameter set, and deciding whether an electronic message is spam or non-spam according to the relevancies and individual classification scores generated by the anti-spam filters. The relevance of an anti-spam filter indicates the degree to which a classification score produced by that particular filter determines the final classification of a given message. The relevance parameter set of each anti-spam filter may include, among others, a training maturity indicative of the degree of training of the filter, a filter update age indicative of the time elapsed since the latest update of the filter, a false-positive classification indicator, and a false-negative classification indicator of the anti-spam filter.

Systems and methods for dynamically integrating heterogeneous anti-spam filters

In some embodiments, image spam is identified by comparing color histograms of suspected spam images with color histograms of reference (known) images. The histogram comparison includes comparing a first color content in a query image with a range of similar color contents in the reference image. For example, a pixel count for a given color in the query image may be compared to pixel counts for a range of similar colors in the reference image. A histogram distance between two images may be determined according to a computed pixel count difference between the given query histogram color and a selected color in the range of similar reference histogram colors.

Image filtering systems and methods

In some embodiments, fully-automated spam identification is facilitated by accelerating a signature extraction process, allowing the use of a relatively large number of signatures finely tailored to individual spam waves, rather than a smaller number of highly-accurate signatures generated under human supervision. The signature extraction process is performed in a distributed manner. A message corpus is classified into a plurality of message clusters. Cluster-specific spam identification text patterns are extracted selectively from members of each cluster, and the text patterns are combined into cluster-specific spam identification signatures. A cluster may represent an individual spam wave. Genetic algorithms are used to optimize the set of spam identification signatures by selecting the highest-performing combinations of cluster-specific spam identification text patterns. Performing signature extraction at a subclass level allows accelerating the signature extraction process, which in turn allows frequent signature updates and facilitates fully automated spam identification.

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Buzau, Romania

Catalin A Cosoi