Detecting malicious code based on static analysis combined with machine learning algorithms
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https://doi.org/10.54939/1859-1043.j.mst.90.2023.134-139Keywords:
Malware; Malware detection; Static analysis; Machine learning algorithms; Abnormal behavior.Abstract
The technique of spreading malicious code through users and then escalating it into the system is increasingly favored by many attackers. Therefore, to detect malicious code, the approach of behavior-based malware detection with the support of machine learning algorithms has proven to be highly effective. On the other hand, in practice, attackers often employ various methods and techniques to conceal the characteristics of malicious code based on the Portable Executable File Format (PE File). This has posed significant challenges for the detection of malware by monitoring systems. For these reasons, in this article, we propose a method for detecting malicious code based on static analysis of PE Files using machine learning algorithms.
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