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A Lightweight IoT Malware Detection and Family Classification Method

Journal of Computer and Communications 2024年第4期12卷 201-227页

作者： Changguang Wang Ziqi Ma Qingru Li Dongmei Zhao Fangwei Wang College of Computer and Cyber Security Hebei Normal University Shijiazhuang China Key Laboratory of Network and Information Security of Hebei Province Hebei Normal University Shijiazhuang China

A lightweight malware detection and family classification system for the Internet of Things (IoT) was designed to solve the difficulty of deploying defense models caused by the limited computing and storage resources of IoT devices. By training complex models with IoT software gray-scale images and utilizing the gradient-weighted class-activated mapping technique, the system can identify key codes that influence model decisions. This allows for the reconstruction of gray-scale images to train a lightweight model called LMDNet for malware detection. Additionally, the multi-teacher knowledge distillation method is employed to train KD-LMDNet, which focuses on classifying malware families. The results indicate that the model’s identification speed surpasses that of traditional methods by 23.68%. Moreover, the accuracy achieved on the Malimg dataset for family classification is an impressive 99.07%. Furthermore, with a model size of only 0.45M, it appears to be well-suited for the IoT environment. By training complex models using IoT software gray-scale images and utilizing the gradient-weighted class-activated mapping technique, the system can identify key codes that influence model decisions. This allows for the reconstruction of gray-scale images to train a lightweight model called LMDNet for malware detection. Thus, the presented approach can address the challenges associated with malware detection and family classification in IoT devices.

关键词： IoT Security Visual Explanations multi-teacher knowledge distillation Lightweight CNN

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