Malware Classification in Local System Executable Files Using Deep Learning

Abstract

One of the biggest and most severe risks on the Internet today is malicious software, generally known as malware. Attackers are producing malware that has the ability to change its source code as it spreads and is polymorphic and metamorphic. Furthermore, the variety and quantity of their variants seriously compromise the effectiveness of current defences, which frequently rely on signature-based techniques and are unable to identify malicious executables that have not yet been detected. Variants from different malware families have behavioural traits that are indicative of their function and place in society. Utilizing the behavioural patterns obtained either statically or dynamically, deep learning techniques can be utilized to discover and classify novel viruses into their recognized families. In this digital age, security failures brought on by malware attacks are on the rise and pose a serious security concern. Malware detection is still a strongly contested academic topic because of the significant implications that malware attacks have on businesses, governments, and computer users. For the real-time identification of unknown malware, the efficacy of current malware detection techniques, which entail the static and dynamic analysis of malware signatures and behaviour patterns, has not been shown. For classifying malware, we mostly utilize CNN and ELM deep learning algorithms.