Data-Aided Intrusion Detection Systems: Leveraging AI, Blockchain and Digital Twin Technology

Alharbi, Ohood, Shaikh, Riaz Ahmed and Asif, Rameez (2025) Data-Aided Intrusion Detection Systems: Leveraging AI, Blockchain and Digital Twin Technology. In: 2024 IEEE International Conference on Big Data (BigData). Proceedings - 2024 IEEE International Conference on Big Data, BigData 2024 . The Institute of Electrical and Electronics Engineers (IEEE), Washington, DC, USA, pp. 8214-8215. ISBN 979-8-3503-6248-0

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Abstract

Intrusion Detection Systems (IDS) are the key for securing the rapidly evolving Internet-of-Things (IoT), where data security and privacy will become increasingly important in the forthcoming era. This research presents an innovative method for improving IDS performance through the integration of Artificial Intelligence (AI), Blockchain, and Digital Twin (DT) technologies. AI is utilized for real-time anomaly detection, whereas DT replicate device behavior for predicting threats and Blockchain ensures secure, decentralized data transmission. Energy-efficient zero-knowledge proofs are employed to meet the energy requirements of Blockchain, enhancing both security and resource efficiency. The performance of the suggested system will be assessed based on detection accuracy, latency, scalability, energy efficiency, and privacy preservation. This distinctive integration of advanced technologies delivers a multi-faceted security system, providing a thorough respond to for strengthening security in IoT networks.

Item Type:	Book Section
Faculty \ School:	Faculty of Science > School of Computing Sciences
UEA Research Groups:	Faculty of Science > Research Groups > FORT-iNET
Related URLs:	http://www.scopus.com/inward/record.url?...
Depositing User:	LivePure Connector
Date Deposited:	24 Feb 2025 15:30
Last Modified:	02 Mar 2025 07:30
URI:	https://ueaeprints.uea.ac.uk/id/eprint/98575
DOI:	10.1109/BigData62323.2024.10825899

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