Behavioral detection of malware: from a survey towards an established taxonomy

摘要

Behavioral detection differs from appearance detection in that it identifies the actions performed by the malware rather than syntactic markers. Identifying these malicious actions and interpreting their final purpose is a complex reasoning process. This paper draws up a survey of the different reasoning techniques deployed among the behavioral detectors. These detectors have been classified according to a new taxonomy introduced inside the paper. Strongly inspired from the domain of program testing, this taxonomy divides the behavioral detectors into two main families: simulation-based and formal detectors. Inside these families, ramifications are then derived according to the data collection mechanisms the data interpretation, the adopted model and its generation, and the decision support.