Malware Detection using Behavioral Whitelisting of Software

Abstract

The detection of malware has been an active area of research for a long period of time. In today’s world of computing, one of the major threats come from different kinds of malware, which makes it imperative to create malware detectors that can sense the presence of malware on our systems. However, with the rapid growth of polymorphic and metamorphic malware, many such malware-detection tools fail quickly or have a high rate of false positives. Our work tackles the problem by creating benign software detectors. Our thesis is that the number of potential malware far outnumbers the number of benign software on a computer system and hence one should detect malware as anomalies in the expected behavior of benign applications instead of trying to build behavioral models for every possible type of malware.M.S., Computer Science -- Drexel University, 201