A New Adversarial Malware Detection Method Based on Enhanced Lightweight Neural Network

作     者：Gao, Caixia Du, Yao Ma, Fan Lan, Qiuyan Chen, Jianying Wu, Jingjing 

作者机构：School of Computer Science and Technology Southwest Minzu University Chengdu China Key laboratory of Computer System State Ethnic Affairs Commission Southwest Minzu University Chengdu610041 China 

出 版 物：《SSRN》 

年 卷 期：2024年

核心收录：

主　　题：Image enhancement 

摘      要：With the gradual expansion of Android systems from mobile phones to intelligent devices, a huge amount of malware has been found every year. To improve the malware detection performance and reduce its reliance on expert experience, deep learning technology has been widely used. However, as the complexity of deep learning models continues to increase, it rapidly increases the consumption of hardware resources. At the same time, anti-detection techniques such as Generative Adversarial Networks (GANs) are widely used to evade Artificial Intelligence (AI)-based detection methods. In this paper, we propose a new classification model based on an improved lightweight neural network that can effectively improve the execution efficiency and detection performance of malware detection methods against adversarial malware samples. First, our method uses local-information-entropy-based image generation technology to construct effective image feature vectors. Then, the performance of the lightweight neural network model ESPNetV2 is improved from four aspects. Finally, a new adversarial malware generation model called Mal-WGANGP is proposed. It can automatically generate a large number of adversarial malware samples for the training and testing of detection methods. In our experiments, we constructed three sets of ablation experiments and compared the detection performance of our method with 19 other novel efficient neural network detection models. Experimental results show that our image enhancement method and detection model are the best in all aspects. © 2024, The Authors. All rights reserved.