CyberNFTs: Conceptualizing a decentralized and reward-driven intrusion detection system with ML
<|reference_start|>CyberNFTs: Conceptualizing a decentralized and reward-driven intrusion detection system with ML: The rapid evolution of the Internet, particularly the emergence of Web3, has transformed the ways people interact and share data. Web3, although still not well defined, is thought to be a return to the decentralization of corporations' power over user data. Despite the obsolescence of the idea of building systems to detect and prevent cyber intrusions, this is still a topic of interest. This paper proposes a novel conceptual approach for implementing decentralized collaborative intrusion detection networks (CIDN) through a proof-of-concept. The study employs an analytical and comparative methodology, examining the synergy between cutting-edge Web3 technologies and information security. The proposed model incorporates blockchain concepts, cyber non-fungible token (cyberNFT) rewards, machine learning algorithms, and publish/subscribe architectures. Finally, the paper discusses the strengths and limitations of the proposed system, offering insights into the potential of decentralized cybersecurity models.<|reference_end|>
@article{selimi2024cybernfts:,
    title={CyberNFTs: Conceptualizing a decentralized and reward-driven intrusion
  detection system with ML},
    author={Synim Selimi, Blerim Rexha and Kamer Vishi},
    journal={International Journal of Information and Computer Security (IJICS)
  2023},
    year={2024},
    doi={10.1504/IJICS.2023.133385},
    number={Vol. 22, No. 1},
    archivePrefix={arXiv},
    eprint={2409.11409},
    primaryClass={cs.CR cs.AI cs.LG}
}