A review of deep learning based malware detection techniques

• Published in: Neurocomputing (Amsterdam) Vol. 598; p. 128010
• Main Authors: Wang, Huijuan, Cui, Boyan, Yuan, Quanbo, Shi, Ruonan, Huang, Mengying
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 14.09.2024
• Subjects: Deep learning; Malware datasets; Malware detection; Deep learning; Malware detection; Malware datasets
• ISSN: 0925-2312; 1872-8286
• DOI: 10.1016/j.neucom.2024.128010

With the popularization of computer technology, the number of malware has increased dramatically in recent years. Some malware can threaten the network security of users by downloading and installing, and even spreading widely on the Internet, causing consequences such as private data leakage in the operating system, extortion, and network paralysis. In order to deal with these threats, researchers analyze malicious samples through various analysis techniques, which are usually divided into static and dynamic analysis based on the principle of whether the code needs to be executed or not. This paper analyzes in detail several classical methods of feature extraction in malware detection techniques. With the technological development of artificial intelligence, deep learning is gradually being introduced into malware detection, which does not require the identification of professional security personnel and greatly improves the generalization ability of detection. In the paper, text-based detection methods, image visualization-based detection, and graph structure-based detection techniques are reviewed according to different feature extraction methods. In addition, the paper compares 26 datasets that have been commonly used in recent years applied in the research field and explains the main contents and specifications of the datasets. Finally, a summary and outlook of the malware research field is given.