A Malicious Code Detection Method Based on Stacked Depthwise Separable Convolutions and Attention Mechanism
Author:
Huang Hong1,
Du Rui1,
Wang Zhaolian1,
Li Xin1,
Yuan Guotao1
Affiliation:
1. School of Computer Science and Engineering, Sichuan University of Science & Engineering, Yibin 644002, China
Abstract
To address the challenges of weak model generalization and limited model capacity adaptation in traditional malware detection methods, this article presents a novel malware detection approach based on stacked depthwise separable convolutions and self-attention, termed CoAtNet. This method combines the strengths of the self-attention module’s robust model adaptation and the convolutional networks’ powerful generalization abilities. The initial step involves transforming the malicious code into grayscale images. These images are subsequently processed using a detection model that employs stacked depthwise separable convolutions and an attention mechanism. This model effectively recognizes and classifies the images, automatically extracting essential features from malicious software images. The effectiveness of the method was validated through comparative experiments using both the Malimg dataset and the augmented Blended+ dataset. The approach’s performance was evaluated against popular models, including XceptionNet, EfficientNetB0, ResNet50, VGG16, DenseNet169, and InceptionResNetV2. The experimental results highlight that the model surpasses other malware detection models in terms of accuracy and generalization ability. In conclusion, the proposed method addresses the limitations of traditional malware detection approaches by leveraging stacked depthwise separable convolutions and self-attention. Comprehensive experiments demonstrate its superior performance compared to existing models. This research contributes to advancing the field of malware detection and provides a promising solution for enhanced accuracy and robustness.
Funder
National Natural Science Foundation of China
Sichuan University of Science & Engineering Talent Project
Open Fund Project of Key Laboratory for Non-Destructive Testing and Engineering Computing of Sichuan Province Universities on Bridge Inspection and Engineering
Sichuan University of Science & Engineering Graduate Student Innovation Fund
Subject
Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry
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