Enhancing Malware Detection Resilience:A U-Net GAN Denoising Framework for Image-Based Classification  

作　　者：Huiyao Dong Igor Kotenko 

机构地区：[1]Faculty of Information Technology and Security,ITMO National Research University,St.Petersburg,197101,Russia [2]Laboratory of Computer Security Problems,St.Petersburg Federal Research Center of the Russian Academy of Sciences,St.Petersburg,199178,Russia

出　　处：《Computers, Materials & Continua》2025年第3期4263-4285,共23页计算机、材料和连续体(英文)

基　　金：funded by the budget project FFZF-2022-0007.

摘　　要：The growing complexity of cyber threats requires innovative machine learning techniques,and image-based malware classification opens up new possibilities.Meanwhile,existing research has largely overlooked the impact of noise and obfuscation techniques commonly employed by malware authors to evade detection,and there is a critical gap in using noise simulation as a means of replicating real-world malware obfuscation techniques and adopting denoising framework to counteract these challenges.This study introduces an image denoising technique based on a U-Net combined with a GAN framework to address noise interference and obfuscation challenges in image-based malware analysis.The proposed methodology addresses existing classification limitations by introducing noise addition,which simulates obfuscated malware,and denoising strategies to restore robust image representations.To evaluate the approach,we used multiple CNN-based classifiers to assess noise resistance across architectures and datasets,measuring significant performance variation.Our denoising technique demonstrates remarkable performance improvements across two multi-class public datasets,MALIMG and BIG-15.For example,the MALIMG classification accuracy improved from 23.73%to 88.84%with denoising applied after Gaussian noise injection,demonstrating robustness.This approach contributes to improving malware detection by offering a robust framework for noise-resilient classification in noisy conditions.

关 键 词：MALWARE CYBERSECURITY deep learning DENOISING 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] TP309[自动化与计算机技术—计算机科学与技术]