SLAM: A Malware Detection Method Based on Sliding Local Attention Mechanism. (25th September 2020)

• Record Type: Journal Article
• Authors: Chen, Jun; Guo, Shize; Ma, Xin; Li, Haiying; Guo, Jinhong; Chen, Ming; Pan, Zhisong
• Other Names: Zhang Yin Academic Editor.
• Is Part Of: Security and communication networks. Volume 2020(2020)
• Publication Date: 2020-09-25
• Subjects: Computer networks -- Security measures -- Periodicals; Computer security -- Periodicals; Cryptography -- Periodicals; 005.805
• DOI: 10.1155/2020/6724513
• Languages: English
• ISSNs: 1939-0114

Abstract

Abstract : Since the number of malware is increasing rapidly, it continuously poses a risk to the field of network security. Attention mechanism has made great progress in the field of natural language processing. At the same time, there are many research studies based on malicious code API, which is also like semantic information. It is a worthy study to apply attention mechanism to API semantics. In this paper, we firstly study the characters of the API execution sequence and classify them into 17 categories. Secondly, we propose a novel feature extraction method based on API execution sequence according to its semantics and structure information. Thirdly, based on the API data characteristics and attention mechanism features, we construct a detection framework SLAM based on local attention mechanism and sliding window method. Experiments show that our model achieves a better performance, which is a higher accuracy of 0.9723.