MLP-Mixer-Autoencoder: A Lightweight Ensemble Architecture for Malware Classification

作     者：Dao, Tuan Van Sato, Hiroshi Kubo, Masao 

作者机构：Natl Def Acad Japan Dept Comp Sci Yokosuka 2398686 Japan 

出 版 物：《INFORMATION》 (信息)

年 卷 期：2023年第14卷第3期

页      面：167页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Malware classification MLP-mixer autoencoder information security 

摘      要：Malware is becoming an effective support tool not only for professional hackers but also for amateur ones. Due to the support of free malware generators, anyone can easily create various types of malicious code. The increasing amount of novel malware is a daily global problem. Current machine learning-based methods, especially image-based malware classification approaches, are attracting significant attention because of their accuracy and computational cost. Convolutional Neural Networks are widely applied in malware classification;however, CNN needs a deep architecture and GPUs for parallel processing to achieve high performance. By contrast, a simple model merely contained a Multilayer Perceptron called MLP-mixer with fewer hyperparameters that can run in various environments without GPUs and is not too far behind CNN in terms of performance. In this study, we try applying an Autoencoder (AE) to improve the performance of the MLP-mixer. AE is widely used in several applications as dimensionality reduction to filter out the noise and identify crucial elements of the input data. Taking this advantage from AE, we propose a lightweight ensemble architecture by combining a customizer MLP-mixer and Autoencoder to refine features extracted from the MLP-mixer with the encoder-decoder architecture of the autoencoder. We achieve overperformance through various experiments compared to other cutting-edge techniques using Malimg and Malheur datasets which contain 9939 (25 malware families) and 3133 variant samples (24 malware families).