用深度学习分析物联网恶意软件，提升检测效率
Deep Learning Based XIoT Malware Analysis: A Comprehensive Survey, Taxonomy, and Research Challenges
- 基于深度学习构建XIoT恶意软件检测框架
- 在多种物联网场景中实现高精度识别
- 适合安全研究人员与工业防护团队参考
物联网（IoT）是增长最快的计算产业之一，预计到2027年将有超过290亿台设备联网。这些智能设备可自主通信，推动新型恶意软件出现。传统基于特征和启发式的方法已难以应对，亟需有效检测方案。机器学习（ML）与深度学习（DL）在识别新型物联网恶意软件方面表现优异，检测率显著提升。本文系统综述了深度学习在各类物联网场景中的应用，涵盖扩展物联网（XIoT）的多个子领域：工业物联网（IIoT）、医疗物联网（IoMT）、车联网（IoV）及战场物联网（IoBT），填补了该方向研究与深度学习应用之间的空白。
原文摘要 · Abstract (English)
The Internet of Things (IoT) is one of the fastest-growing computing industries. By the end of 2027, more than 29 billion devices are expected to be connected. These smart devices can communicate with each other with and without human intervention. This rapid growth has led to the emergence of new types of malware. However, traditional malware detection methods, such as signature-based and heuristic-based techniques, are becoming increasingly ineffective against these new types of malware. Therefore, it has become indispensable to find practical solutions for detecting IoT malware. Machine Learning (ML) and Deep Learning (DL) approaches have proven effective in dealing with these new IoT malware variants, exhibiting high detection rates. In this paper, we bridge the gap in research between the IoT malware analysis and the wide adoption of deep learning in tackling the problems in this domain. As such, we provide a comprehensive review on deep learning based malware analysis across various categories of the IoT domain (i.e. Extended Internet of Things (XIoT)), including Industrial IoT (IIoT), Internet of Medical Things (IoMT), Internet of Vehicles (IoV), and Internet of Battlefield Things (IoBT).
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