Earticle

Malware detection has piqued the interest both academia and anti-malware industry as a result of the malware explosive growth over the past 20 years and the havoc that it has been able to cause. Even though in the past signature-based anti-virus systems have been successful, malware authors and cyber security experts have since been in a never-ending arms race. In order to overcome the endeavors of cyber security experts, malware authors created polymorphic, metamorphic, and oligomorphic engines for malware in order to bypass the detection of traditional anti-virus systems. As a result, cyber security experts sought to devise their best strategies for retaliating against adversary. Conventional algorithms of machine learning and more complex ones of deep learning constitute the remedy to such impediment. The major vulnerability of machine learning-based malware detection systems is represented by adversarial examples. In this paper, we propose a machine learning-based malware detection system that is resistant to adversarial malware by utilising code normalisation. We evaluate the impact of code normalisation in a deep learning based-malware detection system and the proposed malware detection system with the code normalisation scored 99.02% success rate.