Abstract: In some embodiments, a layout-based electronic communication classification (e.g. spam filtering) method includes generating a layout vector characterizing a layout of a message, assigning the message to a selected cluster according to a hyperspace distance between the layout vector and a central vector of the selected cluster, and classifying the message (e.g. labeling as spam or non-spam) according to the selected cluster. The layout vector is a message representation characterizing a set of relative positions of metaword substructures of the message, as well as metaword substructure counts. Examples of metaword substructures include MIME parts and text lines. For example, a layout vector may have a first component having scalar axes defined by numerical layout feature counts (e.g. numbers of lines, blank lines, links, email addresses), and a second vector component including a line-structure list and a formatting part (e.g. MIME part) list.

Abstract: 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.

Abstract: In some embodiments, a layout-based electronic communication classification (e.g. spam filtering) method includes generating a layout vector characterizing a layout of a message, assigning the message to a selected cluster according to a hyperspace distance between the layout vector and a central vector of the selected cluster, and classifying the message (e.g. labeling as spam or non-spam) according to the selected cluster. The layout vector is a message representation characterizing a set of relative positions of metaword substructures of the message, as well as metaword substructure counts. Examples of metaword substructures include MIME parts and text lines. For example, a layout vector may have a first component having scalar axes defined by numerical layout feature counts (e.g. numbers of lines, blank lines, links, email addresses), and a second vector component including a line-structure list and a formatting part (e.g. MIME part) list.

Abstract: 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.