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CyberNFTs: Conceptualizing a decentralized and reward-driven intrusion detection system with ML

Synim Selimi, Blerim Rexha, Kamer Vishi

TL;DR

The paper addresses how to realize a decentralized intrusion detection framework by merging Web3 technologies, ML, and a tokenized reward system. It proposes a CIDN where local network traffic is analyzed with ML, results and signatures are stored on a blockchain, and discovery ownership is tracked via CyberNFTs within a publish/subscribe network. Through a proof-of-concept Autonom system, the authors demonstrate end-to-end operation and compare decentralized versus centralized approaches, highlighting both potential benefits and current limitations. The work offers a foundation for decentralized cybersecurity models and identifies key challenges—particularly around blockchain scalability and cross-node model updates—that future research must address to achieve a fully decentralized, scalable IDS ecosystem.

Abstract

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.

CyberNFTs: Conceptualizing a decentralized and reward-driven intrusion detection system with ML

TL;DR

The paper addresses how to realize a decentralized intrusion detection framework by merging Web3 technologies, ML, and a tokenized reward system. It proposes a CIDN where local network traffic is analyzed with ML, results and signatures are stored on a blockchain, and discovery ownership is tracked via CyberNFTs within a publish/subscribe network. Through a proof-of-concept Autonom system, the authors demonstrate end-to-end operation and compare decentralized versus centralized approaches, highlighting both potential benefits and current limitations. The work offers a foundation for decentralized cybersecurity models and identifies key challenges—particularly around blockchain scalability and cross-node model updates—that future research must address to achieve a fully decentralized, scalable IDS ecosystem.

Abstract

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.
Paper Structure (14 sections, 4 equations, 7 figures, 1 table, 1 algorithm)

This paper contains 14 sections, 4 equations, 7 figures, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. Defining the problem
  3. Breakthrough of ML and collaborative IDS
  4. Intrusion detection systems (IDS)
  5. Network traffic analysis with ML
  6. Data schema
  7. Decentralization
  8. Blockchain
  9. CyberNFTs
  10. Case Study – Autonom
  11. Integration
  12. Distributed operation (pub/sub)
  13. Discussion
  14. Conclusion

Figures (7)

  • Figure 1: Proportion of businesses and charities reporting cyber intrusions according to UK annual report 2022 gov2022cyber
  • Figure 2: The conceptual architecture of the system and essential components
  • Figure 3: Building Blocks of Distributed Features in IDS with Blockchain (e-health application use case) ajayi2021blockchain
  • Figure 4: Architecture of an anomaly detection engine. Adapted from bhuyan2014network
  • Figure 5: Logarithmic relationship of block generation time against difficulty level
  • ...and 2 more figures