A Survey of different machine learning models for static and dynamic malware detection
European Journal of Molecular & Clinical Medicine,
2020, Volume 7, Issue 3, Pages 4299-4308
AbstractMalicious software (malware) plays a vital role in cybercrime security. As the number of malicious attacks and its target sources is increasing, it is difficult to find and prevent the attack due to its change in behaviour. Most of the traditional malware detection models are based on the statistical, analytical, and machine learning models. Detection of malware usually utilizes virus signature methods to defend against malicious software. Most antivirus tools to categorize malware depend on regular expression and pattern. Antivirus is less likely to update their databases to detect and prevent malware as file features have to update a newly created malware. The practically maximum human effort was required in order to generate attack signatures. In this paper, different types of malware detection models and their problems are discussed. This paper provides an extensive survey on the malware attack detection using traditional supervised, unsupervised models. Different types of malware attacks and their variations in behaviour are discussed in offline and online systems.
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