MALWARE DETECTION USING MACHINE LEARNING ALGORITHMS

Abstract

Malware is a serious threat that weakens computer security. Malware detection is a branch of computer security concerned with the investigation and prevention of malicious software. It is not the only way to protect a company from a cyber-attack. Companies must analyse their risks and vulnerabilities in order to be effective. With this increase in cyber fraud and other malicious activities, traditional methods are not enough to block computers from it as this method has many drawbacks. In order to tackle these issues, researchers have been developing new techniques such as heuristic analysis, and static & dynamic analysis which can detect more than 90% of malware samples. In this paper, we will look at various techniques for detecting computer malware and malicious websites, as well as a versatile framework in which different machine-learning algorithms can be used to successfully distinguish between malware and clean files while attempting to reduce the number of false positives. In this paper, we have reviewed the rise of computer malware and how traditional methods of detection are being replaced by innovative techniques such as the behavioural-based model and the signature-based model.