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Malware Detection Using Dual Siamese Network Model

Computer Modeling in Engineering & Sciences 2024年第10期141卷 563-584页

作者： ByeongYeol An jeahyuk yang Seoyeon Kim Taeguen Kim Department of Software Convergence Soonchunhyang UniversityAsan31538Republic of Korea Department of Mobility Convergence Security Soonchunhyang UniversityAsan31538Republic of Korea Department of Information Security Engineering Soonchunhyang UniversityAsan31538Republic of Korea

This paper proposes a new approach to counter cyberattacks using the increasingly diverse malware in cyber *** signature detection methods that utilize static and dynamic features face limitations due to the continuous evolution and diversity of new ***,machine learning-based malware detection techniques,such as Convolutional Neural Networks(CNN)and Recurrent Neural Networks(RNN),have gained *** these methods demonstrate high performance by leveraging static and dynamic features,they are limited in detecting new malware or variants because they learn based on the characteristics of existing *** overcome these limitations,malware detection techniques employing One-Shot Learning and Few-Shot Learning have been *** on this,the Siamese Network,which can effectively learn from a small number of samples and perform predictions based on similarity rather than learning the characteristics of the input data,enables the detection of new malware or *** propose a dual Siamese network-based detection framework that utilizes byte images converted frommalware binary data to grayscale,and opcode frequency-based images generated after extracting opcodes and converting them into *** proposed framework integrates two independent Siamese network models,one learning from byte images and the other from opcode frequency-based *** detection models trained on the different kinds of images generated separately apply the L1 distancemeasure to the output vectors themodels generate,calculate the similarity,and then apply different weights to each *** proposed framework achieved a malware detection accuracy of 95.9%and 99.83%in the experimentsusingdifferentmalware *** experimental resultsdemonstrate that ourmalware detection model can effectively detect malware by utilizing two different types of features and employing the dual Siamese network-based model.

关键词： Siamese network malware detection few-shot learning

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