Novel active learning methods for enhanced PC malware detection in windows OS

Nir Nissim, Robert Moskovitch, Lior Rokach, Yuval Elovici

Expert Systems with Applications 41 (13), 5843-5857, 2014

The formation of new malwares every day poses a significant challenge to anti-virus vendors since antivirus tools, using manually crafted signatures, are only capable of identifying known malware instances and their relatively similar variants. To identify new and unknown malwares for updating their anti-virus signature repository, anti-virus vendors must daily collect new, suspicious files that need to be analyzed manually by information security experts who then label them as malware or benign. Analyzing suspected files is a time-consuming task and it is impossible to manually analyze all of them. Consequently, anti-virus vendors use machine learning algorithms and heuristics in order to reduce the number of suspect files that must be inspected manually. These techniques, however, lack an essential element – they cannot be daily updated. In this work we introduce a solution for this updatability gap. We present …