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arXiv 2408.04725cs.CR
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人工智能与后量子时代面向下一代网络的拒绝服务攻击对抗

Counter Denial of Service for Next-Generation Networks within the Artificial Intelligence and Post-Quantum Era

Saleh Darzi, Attila A. Yavuz

AI总结:

本文系统梳理了人工智能与后量子时代下一代网络中的DoS攻击对抗方法,指出当前研究的不足,并探讨了AI入侵检测、联邦学习、区块链及主动防御等协同技术。

摘要:

鉴于网络系统面临的网络威胁日益增多,加之人工智能技术的普及和增强的处理能力,拒绝服务(DoS)攻击正变得越来越复杂且易于实施。它们以系统可用性为目标,在不破坏底层安全协议的情况下使整个系统受损。因此,大量研究集中于预防、检测和缓解 DoS 攻击。然而,最先进的系统化工作存在局限性,例如孤立的 DoS 对抗措施、基于人工智能的研究存在不足,以及缺乏隐私、匿名性、认证和透明度等 DoS 集成特性。此外,量子计算机的出现从攻击和防御的角度来看都是 DoS 领域的变革因素,但这一方面在很大程度上仍未得到探索。本研究旨在通过考察人工智能时代的(对抗)DoS 来弥补这些空白,同时在适用时考虑后量子(PQ)安全。我们指出现有文献中的不足,并提供关于协同技术以弥合这些空白的见解。我们探索用于 DoS 入侵检测的人工智能机制,评估前沿机器学习模型中的网络安全属性,并分析 DoS 背景下武器化的人工智能。我们还通过联邦学习和区块链研究协作式与分布式对抗 DoS 框架。最后,我们评估可集成到下一代网络系统中用于 DoS 预防和缓解的主动方法,如蜜罐、谜题和认证方案。

英文摘要:

Given the rise in cyber threats to networked systems, coupled with the proliferation of AI techniques and enhanced processing capabilities, Denial of Service (DoS) attacks are becoming increasingly sophisticated and easily executable. They target system availability, compromising entire systems without breaking underlying security protocols. Consequently, numerous studies have focused on preventing, detecting, and mitigating DoS attacks. However, state-of-the-art systematization efforts have limitations such as isolated DoS countermeasures, shortcomings of AI-based studies, and a lack of DoS integration features like privacy, anonymity, authentication, and transparency. Additionally, the emergence of quantum computers is a game changer for DoS from attack and defense perspectives, yet it has remained largely unexplored. This study aims to address these gaps by examining (counter)-DoS in the AI era while also considering post-quantum (PQ) security when it applies. We highlight the deficiencies in the current literature and provide insights into synergistic techniques to bridge these gaps. We explore AI mechanisms for DoS intrusion detection, evaluate cybersecurity properties in cutting-edge machine learning models, and analyze weaponized AI in the context of DoS. We also investigate collaborative and distributed counter-DoS frameworks via federated learning and blockchains. Finally, we assess proactive approaches such as honeypots, puzzles, and authentication schemes that can be integrated into next-generation network systems for DoS prevention and mitigation.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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