Detecting Malware Using Deep Neural Networks

This article proposes a method for detecting malicious executable files by analyzing disassembled code. This method is based on a static analysis of assembler instructions of executable files using a special neural network model, whose architecture is also presented in this article. In addition, the...

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Veröffentlicht in:Automatic control and computer sciences Jg. 58; H. 8; S. 1147 - 1155
Hauptverfasser: Ovasapyan, T. D., Volkovskii, M. A., Makarov, A. S.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Moscow Pleiades Publishing 01.12.2024
Springer Nature B.V
Schlagworte:
Artificial neural networks
Classification
Cloning
Computer Science
Control Structures and Microprogramming
Dictionaries
Malware
Methods
Neural networks
Software
static analysis
transformer
malware detection
disassembly analysis
BERT
machine learning
deep neural networks
ISSN:0146-4116, 1558-108X
Online-Zugang:Volltext
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Zusammenfassung:This article proposes a method for detecting malicious executable files by analyzing disassembled code. This method is based on a static analysis of assembler instructions of executable files using a special neural network model, whose architecture is also presented in this article. In addition, the effectiveness of the method is demonstrated using several different metrics, showing a significant reduction in Type-II errors compared to other state-of-the-art methods. The obtained results can be used as a basis for designing systems for thestatic analysis of malware.
Bibliographie:ObjectType-Article-1
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ISSN:0146-4116
1558-108X
DOI:10.3103/S0146411624700779