№1, 2023


Elshan O. Baghirov

The amount of new malware has been continuously growing, and its threats are increasing rapidly. Developing new types of detection methods and thereby protecting computer systems from malicious programs has always been of interest to scientific researchers, individuals and organizations. In this work, several classification methods are applied on the dataset which is prepared on the basis of opcodes obtained from known malicious and benign program samples. Dependency between opcodes higher than 70% of total are removed to achieve more relevant results. The other main factors affecting the results of the methods are evaluated. Results prove that Random Forest classifier can classify suspicious programs with higher accuracy than others (pp.3-7).

Keywords: Opcode frequency, correlation, malware, signature, obfuscation
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