Version 1
: Received: 11 March 2024 / Approved: 12 March 2024 / Online: 12 March 2024 (14:00:37 CET)
How to cite:
Ismail, S.; Moudoud, H.; Dawoud, D.; Reza, H. Blockchain-Based Zero Trust Supply Chain Security Integrated with Deep Reinforcement Learning. Preprints2024, 2024030714. https://doi.org/10.20944/preprints202403.0714.v1
Ismail, S.; Moudoud, H.; Dawoud, D.; Reza, H. Blockchain-Based Zero Trust Supply Chain Security Integrated with Deep Reinforcement Learning. Preprints 2024, 2024030714. https://doi.org/10.20944/preprints202403.0714.v1
Ismail, S.; Moudoud, H.; Dawoud, D.; Reza, H. Blockchain-Based Zero Trust Supply Chain Security Integrated with Deep Reinforcement Learning. Preprints2024, 2024030714. https://doi.org/10.20944/preprints202403.0714.v1
APA Style
Ismail, S., Moudoud, H., Dawoud, D., & Reza, H. (2024). Blockchain-Based Zero Trust Supply Chain Security Integrated with Deep Reinforcement Learning. Preprints. https://doi.org/10.20944/preprints202403.0714.v1
Chicago/Turabian Style
Ismail, S., Diana Dawoud and Hassan Reza. 2024 "Blockchain-Based Zero Trust Supply Chain Security Integrated with Deep Reinforcement Learning" Preprints. https://doi.org/10.20944/preprints202403.0714.v1
Abstract
The modern supply chain (SC) is growing in terms of data, devices, users, and stakeholders, which introduced new security challenges and threats, especially with the reliance on centralized servers or cloud platforms. In addition, increased trust among system participants exposes the SC to a higher risk of vulnerabilities which require strong security measures. This article proposes a hybrid security framework for SC systems, BC-DRLzSC, that integrates Blockchain (BC) and Deep Reinforcement Learning (DRL) designed to operate in a zero trust (ZT) environment. In particular, we propose a decentralized BC-based approach integrated with smart contracts to manage system participant registration and authentication and to control access to system resources. BC-DRLzSC adopts a ZT architecture to reinforce SC security, which can be achieved with an advocate to verify each entity’s trustworthiness before granting or retaining access to system resources. Incorporating the ZT architecture, with BC and DRL, can potentially and significantly bolster SC system security. DRL is employed to develop a proactive attack detection model that continuously monitors the incoming traffic from authenticated nodes within the network and predicts any malicious actions. Finally, we evaluate the performance of our proposed DRL solution using the NSL-KDD dataset.
Keywords
Security; Zero Trust; Blockchain; Smart Contract; Supply Chain; Deep Reinforcement Learning; Cyberattacks
Subject
Computer Science and Mathematics, Security Systems
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
