On Development of Malware Threats Detection and Prevention using artificial Immune System and Machine Learning Algorithm

Abstract

Malare threats detection and prevention using artificial immune system and machine learning is a research work aimed at developing a system to enhance the security of the computer systems. With the rapid technological advancement, security has become a major issue due to the increase in malware activity that poses a serious threat to the security and safety of both computer systems and stakeholders. A set of malicious programming code, scripts, active content or intrusive software that is designed to destroy intended computer systems and programs is referred to as malware. According to a study, naive users are unable to distinguish between malicious and genuine applications. Thus, computer systems should be designed to detect malicious activities towards protecting the system from threats. A number of algorithms are available to detect malware activities by utilizing novel concepts including Artificial Intelligence, Machine Learning, and Deep Learning. In this study, Artificial immune system and machine learning algorithm was used for detecting and preventing malware activity. The system development utilized expert system methodology. The research work shows that adopting hybrid approaches for the development of malware detection applications provided significant advantages as 91.85% accuracy in maleware threat detection was achieved.

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