Integrated Collaborative Intrusion Detection System Using Ensemble Learning Algorithms
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As the internet-connected systems have expanded, albeit briefly, intrusion detection systems (IDS) have become an important element in cybersecurity. Conventional host-based and network-based IDS systems are unable to detect advanced and distributed attacks promptly. Collaborative Intrusion Detection Systems (CIDS) enhance precision by enabling nodes to share intelligence. Nevertheless, CIDS usually have issues associated with secure data sharing and trust. To overcome these constraints, this paper presents a smart, collaborative intrusion detection system based on the Ethereum blockchain. Decentralised trust, data immutability, and the absence of a central authority are guaranteed through blockchain integration. Both the signature-matching and fuzzy genetic algorithms are machine learning algorithms used in anomaly- and signature-based intrusion detection. Datasets such as NSL KDD, CIC IDS 2017, and CIC IDS 2018 are used to assess the system's performance. Findings show better accuracy, fewer false positives and greater resilience in the multi-node environments. The suggested architecture will include IDS tools such as Snort, Zeek, and Suricata, and will be combined with smart contracts to enable secure cooperation. The given work contributes to the development of the field by combining AI, blockchain, and CIDs to provide a new solution to the current threats posed by cybersecurity violations in distributed networks.
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