Malware detection using inverse imbalance subspace searching

Abstract

Inverse imbalance subspace searching techniques are used to detect potential malware among samples of network communication data. A large number of samples of network communication data, such as proxy log data and/or network flows, are received and analyzed by a malware detection system. A number of the samples are associated with known malware, while other unlabeled samples are either benign or may be associated with unknown malware. An inverse imbalance subspace search may be performed, in which the sample sets are divided into subsets based on random feature thresholds, and each subset is evaluated based on the ratio of known malware samples to unlabeled samples. Unlabeled samples within subsets having high malware sample ratios may be identified, aggregated, and processed as potential malware.