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Collaborative Intrusion Detection in IoT by Integrating Blockchain and Semi-Supervised Learning
DOI: https://doi.org/10.62381/I245902
Author(s)
Ruozheng Wu1, Zexiang Liu1, Ziao Dong1, Yanbing Liang1,2,*
Affiliation(s)
1College of Science, North China University of Science and Technology, Tangshan, Hebei, China 2Hebei Key Laboratory of Data Science and Application, North China University of Science and Technology, Tangshan, Hebei, China *Corresponding Author
Abstract
With the proliferation of Internet of Things (IoT) devices, traditional intrusion detection systems (IDS) face challenges such as widespread data distribution and high labeling costs. This paper proposes a blockchain-based semi-supervised collaborative intrusion detection system (B-IDS) that integrates blockchain technology with a semi-supervised Generative Adversarial Network (SGAN) model to enhance intrusion detection performance in IoT environments. B-IDS leverages the decentralized, tamper-proof, and traceable characteristics of blockchain to achieve secure data sharing and collaborative detection. Additionally, it utilizes InterPlanetary File System (IPFS) for large-scale data storage, alleviating the data transmission bottleneck associated with blockchain. Experimental results demonstrate that B-IDS outperforms traditional models in accuracy, recall, and F1 score, effectively enhancing the system's detection accuracy and resilience against attacks. This system exhibits high adaptability and application potential, offering a new solution for intrusion detection in IoT.
Keywords
Blockchain; IoT; IDS; SGAN; Collaborative Detection; IPFS
References
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