Machine Learning Techniques for Malware Detection

Abstract

The introduction of Transport Layer Security has been one of the most important contributors to the privacy and security of internet communications during the last decade. Malware authors have followed suit, using TLS to hide potentially dangerous network connections. Because of the growing use of encryption and other evasion measures, traditional content-based network traffic categorization is becoming more challenging. In this paper, we provide a malware classification technique that uses packet information and machine learning algorithms to detect malware. We employ the use of classification algorithms such as support vector machine and random forest. We start by eliminating characteristics that are highly correlated. We utilized the Random Forest method to choose only the 10 best characteristics from all the remaining features after eliminating the unnecessary ones. Following the feature selection phase, we employ several classification algorithms and evaluate their performance. Random forest algorithm performed exceptionally well in our experiments resulting in an accuracy score of over 0.99.