Blockchain-aided Collaborative Threat Detection for Securing Digital Twin-based IIoT Networks

• Main Authors: Zainudin, Ahmad, Paramartha Putra, Made Adi, Alief, Revin Naufal, Kim, Dong-Seong, Lee, Jae-Min
• Format: Conference Proceeding
• Language: English
• Subjects: Accuracy; blockchain; Blockchains; Collaboration; Collaborative intrusion detection; Digital signatures; digital twin; industrial internet of things (IIoT); Network intrusion detection; Robustness; Threat assessment; verifiable off-chain aggregation
• ISSN: 1938-1883
• DOI: 10.1109/ICC51166.2024.10622717

The distributed and heterogeneous connections in the digital twin (DT)-based industrial Internet of Things (IIoT) are vulnerable to cyber-attacks and malicious activities. This study proposes a permissioned blockchain-assisted collaborative and decentralized cyber threat detection for securing DT-based IIoT networks. A context-aware network intrusion detection system (C-NIDS) model was developed using factorized and grouped convolution structures to detect adversarial attacks in virtual and physical environments. A verifiable off-chain aggregation technique with a digital signature is implemented to provide a trustworthy and anti-tampering aggregated model with minimum transaction time. The results exhibit the robustness of the proposed model by achieving an attack detection accuracy of 99.50% using a lightweight model structure with trainable parameters of 4,634 and MFLOPs calculation of 0.0088. Moreover, the verifiable off-chain aggregation performs a total transaction time of 0.0244 seconds.