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

TL;DR

This paper proposes a novel decentralized intrusion detection system leveraging Web3, blockchain, cyberNFT rewards, and machine learning, aiming to enhance cybersecurity through collaborative, reward-driven networks.

Contribution

It introduces a conceptual model combining blockchain, cyberNFTs, and ML for decentralized intrusion detection, which is a novel integration in cybersecurity.

Findings

Demonstrates the feasibility of a decentralized CIDN using blockchain and cyberNFTs.

Highlights the potential benefits and limitations of Web3-based cybersecurity models.

Provides a proof-of-concept for integrating ML with decentralized networks.

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.