Deep Learning in the Fast Lane: A Survey on Advanced Intrusion Detection Systems for Intelligent Vehicle Networks

IF 5.3 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Mohammed Almehdhar;Abdullatif Albaseer;Muhammad Asif Khan;Mohamed Abdallah;Hamid Menouar;Saif Al-Kuwari;Ala Al-Fuqaha
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引用次数: 0

Abstract

The rapid evolution of modern automobiles into intelligent and interconnected entities presents new challenges in cybersecurity, particularly in Intrusion Detection Systems (IDS) for In-Vehicle Networks (IVNs). This survey paper offers an in-depth examination of advanced machine learning (ML) and deep learning (DL) approaches employed in developing sophisticated IDS for safeguarding IVNs against potential cyber-attacks. Specifically, we focus on the Controller Area Network (CAN) protocol, which is prevalent in in-vehicle communication systems, yet exhibits inherent security vulnerabilities. We propose a novel taxonomy categorizing IDS techniques into conventional ML, DL, and hybrid models, highlighting their applicability in detecting and mitigating various cyber threats, including spoofing, eavesdropping, and denial-of-service attacks. We highlight the transition from traditional signature-based to anomaly-based detection methods, emphasizing the significant advantages of AI-driven approaches in identifying novel and sophisticated intrusions. Our systematic review covers a range of AI algorithms, including traditional ML, and advanced neural network models, such as Transformers, illustrating their effectiveness in IDS applications within IVNs. Additionally, we explore emerging technologies, such as Federated Learning (FL) and Transfer Learning, to enhance the robustness and adaptability of IDS solutions. Based on our thorough analysis, we identify key limitations in current methodologies and propose potential paths for future research, focusing on integrating real-time data analysis, cross-layer security measures, and collaborative IDS frameworks.
快车道上的深度学习:智能车联网高级入侵检测系统概览
现代汽车迅速发展成为智能互联实体,给网络安全带来了新的挑战,尤其是车载网络(IVN)的入侵检测系统(IDS)。本调查报告深入探讨了先进的机器学习(ML)和深度学习(DL)方法,这些方法用于开发先进的 IDS,以保护 IVN 免受潜在的网络攻击。具体而言,我们将重点放在控制器局域网(CAN)协议上,该协议在车载通信系统中非常普遍,但却存在固有的安全漏洞。我们提出了一种新的分类法,将 IDS 技术分为传统的 ML、DL 和混合模型,强调它们在检测和缓解各种网络威胁(包括欺骗、窃听和拒绝服务攻击)方面的适用性。我们重点介绍了从传统的基于签名的检测方法到基于异常的检测方法的转变,强调了人工智能驱动的方法在识别新型和复杂入侵方面的显著优势。我们的系统性综述涵盖了一系列人工智能算法,包括传统的 ML 和高级神经网络模型(如 Transformers),说明了它们在 IVN 内 IDS 应用中的有效性。此外,我们还探索了联邦学习(FL)和迁移学习等新兴技术,以增强 IDS 解决方案的鲁棒性和适应性。在全面分析的基础上,我们确定了当前方法的主要局限性,并提出了未来研究的潜在路径,重点是整合实时数据分析、跨层安全措施和协作式 IDS 框架。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
9.60
自引率
0.00%
发文量
25
审稿时长
10 weeks
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