A Smart System of Malware Detection Based on Artificial Immune Network and Deep Belief Network

This paper proposes a smart system of virus detection that can classify a file as benign or malware with high accuracy detection rate. The approach is based on the aspects of the artificial immune system and the deep learning technique. The first stage is data extraction to create the main feature set. In the second stage, the Artificial Immune Network (aiNet) is used to build a clonal generation of malware detectors and improve the accuracy of unknown virus detection rate. Then they are trained with a deep belief network model to evaluate the performance of the system. As a result, our method can achieve a high detection rate of 98.86% on average with a very low false positive rate.