A Novel Deep Learning-Based Malware Detection Scheme Considering Packers and Encryption

Abstract

With the continuous improvement of the current level of information technology, the malicious software produced by attackers is also becoming more complex. It's difficult for computer users to protect themselves against malicious software attacks. Malicious software can steal the user's privacy, damage the user's computer system, and often cause serious consequences and huge economic losses to the user or the organization. Hence, this research study presents a novel deep learning-based malware detection scheme considering packers and encryption. The proposed model has 2 aspects of innovations: (1) Generation steps of the packer malware is analyzed. Packing involves adding code to the program to be protected, and original program is compressed and encrypted during the packing process. By understanding this step, the analysis of the software will be efficient. (2) The deep learning based detection model is designed. Through the experiment compared with the latest methods, the performance is proven to be efficient.