Abstract: A two stage virus detection system detects viruses in target files. In the first stage, a training application receives a master virus pattern file recording all known virus patterns and generates a features list containing fundamental virus signatures from the virus patterns, a novelty detection model, a classification model, and a set of segmented virus pattern files. In the second stage, a detection application scans a target file for viruses using the generated outputs from the first stage rather than using the master virus pattern file directly to do traditional pattern matching. The results of the scan can vary in detail depending on a fuzzy scan level. For fuzzy scan level “1,” the existence of a virus is returned. For fuzzy scan level “2,” the grant virus type found is returned. For fuzzy scan level “3,” the exact virus name is returned.

Abstract: In one embodiment, a content filtering system generates a support vector machine (SVM) learning model in a server computer and provides the SVM learning model to a mobile phone for use in classifying text messages. The SVM learning model may be generated in the server computer by training a support vector machine with sample text messages that include spam and legitimate text messages. A resulting intermediate SVM learning model from the support vector machine may include a threshold value, support vectors and alpha values. The SVM learning model in the mobile phone may include the threshold value, the features, and the weights of the features. An incoming text message may be parsed for the features. The weights of features found in the incoming text message may be added and compared to the threshold value to determine whether or not the incoming text message is spam.