Machine Learning Techniques for Malware Detection

Abstract

Abstract—Malware is a term used to describe the types of malicious software that can be used to infect a single computer or the network of an entire company. Viruses and malware are among the most serious risks to online safety that exist right now. There is a serious threat to world security since the volume of malware is increasing at an alarming rate. In order to prevent detection by any antivirus software, all current malware applications tend to incorporate several polymorphic layers or side mechanisms that automatically update themselves at short intervals so that they can remain undetected for longer periods of time. For the identification of malware, we present a flexible framework that allows the use of various machine learning methods, such as decision trees, random forests, and so on. The system's detection rate is greatly improved by selecting the algorithm with the highest level of accuracy. False positive and false negative rates are calculated using the confusion matrix in order to evaluate the system' s overall performance. Keywords—Malware, Malware Detection, Machine Learning, Malware Analysis