Blockchain based AI-enabled Industry 4.0 CPS Protection against Advanced Persistent Threat
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Abstract (arXiv)
Industry 4.0 is all about doing things in a concurrent, secure, and fine-grained manner. IoT edge-sensors and their associated data play a predominant role in today's industry ecosystem. Breaching data or forging source devices after injecting advanced persistent threats (APT) damages the industry owners' money and loss of operators' lives. The existing challenges include APT injection attacks targeting vulnerable edge devices, insecure data transportation, trust inconsistencies among stakeholders, incompliant data storing mechanisms, etc. Edge-servers often suffer because of their lightweight computation capacity to stamp out unauthorized data or instructions, which in essence, makes them exposed to attackers. When attackers target edge servers while transporting data using traditional PKI-rendered trusts, consortium blockchain (CBC) offers proven techniques to transfer and maintain those sensitive data securely. With the recent improvement of edge machine learning, edge devices can filter malicious data at their end which largely motivates us to institute a Blockchain and AI aligned APT detection system. The unique contributions of the paper include efficient APT detection at the edge and transparent recording of the detection history in an immutable blockchain ledger. In line with that, the certificateless data transfer mechanism boost trust among collaborators and ensure an economical and sustainable mechanism after eliminating existing certificate authority. Finally, the edge-compliant storage technique facilitates efficient predictive maintenance. The respective experimental outcomes reveal that the proposed technique outperforms the other competing systems and models.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). First posted on arXiv 30 Jan 2022.
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Citing papers per year: 2022: 1; 2023: 0; 2024: 0; 2025: 2.
3 papers.
- 2022XAI for Cybersecurity: State of the Art, Challenges, Open Issues and Future Directionscited by 10
- 2025A Survey on Cloud-Edge-Terminal Collaborative Intelligence in AIoT Networkscited by 3
- 2025A Study on the Importance of Features in Detecting Advanced Persistent Threats Using Machine Learningcited by 0
- 2025A Survey on Cloud-Edge-Terminal Collaborative Intelligence in AIoT Networks26 Aug 2025
- 2025A Study on the Importance of Features in Detecting Advanced Persistent Threats Using Machine Learning11 Feb 2025
- 2022XAI for Cybersecurity: State of the Art, Challenges, Open Issues and Future Directions3 Jun 2022