Counter Denial of Service for Next-Generation Networks within the Artificial Intelligence and Post-Quantum Era
<|reference_start|>Counter Denial of Service for Next-Generation Networks within the Artificial Intelligence and Post-Quantum Era: 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.<|reference_end|>
@article{darzi2024counter,
    title={Counter Denial of Service for Next-Generation Networks within the
  Artificial Intelligence and Post-Quantum Era},
    author={Saleh Darzi, Attila A. Yavuz},
    journal={arXiv preprint arXiv:2408.04725},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.04725},
    primaryClass={cs.CR}
}