Construction and evaluation of the new heuristic malware detection mechanism based on executable files static analysis

The paper presents the application justification of a new set of features collected at the stage of the static analysis of the executable files to address the problem of malicious code detection. In the course of study the following problems were solved: the development of the executable files classifier in the absence of a priori data concerning their functionality; designing the class models of uninfected files and malware during the learning process; the development of malicious code detection procedure using the neural networks mathematical apparatus and decision tree composition relating to the set of features specified on the basis of the executable files static analysis. The paper contains the results of experimental evaluation of the developed detection mechanism efficiency on the basis of neural networks (accuracy was 0.99125) and decision tree composition (accuracy was 0.99240). The obtained data confirmed the hypothesis about the possibility of constructing the heuristic malware analyzer on the basis of features selected during the static analysis of the executable files.