Information technology enhances efficiency and introduces the threat of malware. A typical means of destruction, malware affects the operational security of information systems and poses certain risks to human life, production, and social operations. One of the key research directions in the field of information security is the identification and classification of malware. Currently, mainstream malware analysis and identification methods fall into static and dynamic categories. As a result of obfuscated system calls, it is difficult to detect malware using static analysis, and traditional dynamic analysis methods based on local features are not sufficiently accurate. This paper combines sequence-based LSTM neural networks with learning algorithms for traditional API call features to study malware classification. Furthermore, this paper discusses the idea of hybrid classification based on LSTM and expands the research in this area to some extent. As a result of the research presented in this paper, dynamic analysis and classification of malware are expected to be more effective.

Bochun Hu, Yuqing Yang, Jinghao Wei, Bin Wu. Research and Application of Malware Classification Method Based on LSTM. Academic Journal of Computing & Information Science (2022), Vol. 5, Issue 8: 95-100. https://doi.org/10.25236/AJCIS.2022.050814.

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