Integrating Explainable Artificial Intelligence with Blockchain-Backed Cybersecurity Architectures for Transparent, Traceable, and Secure Systems
Keywords:
Explainable AI, Blockchain, Cybersecurity, Transparency, Traceability, Secure SystemsAbstract
This paper explores the integration of Explainable Artificial Intelligence (XAI) with blockchain-backed cybersecurity architectures, aimed at creating systems that are transparent, traceable, and secure. By leveraging XAI’s interpretability and blockchain’s immutability, this approach addresses critical challenges in data security, auditability, and trust. We analyze past research, current developments, and future opportunities in this domain. The paper also discusses potential challenges and proposes a framework to implement these technologies effectively.
References
Nakamoto, Satoshi. "Bitcoin: A Peer-to-Peer Electronic Cash System." 2008.
Paramasivan, A. (2024). Enhancing customer trust in card payments: AI-based risk management models. International Journal of Leading Research Publication (IJLRP), 5(10), 1–11.
Ribeiro, Marco Tulio, Sameer Singh, and Carlos Guestrin. "‘Why Should I Trust You ?’: Explaining the Predictions of Any Classifier." Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016.
Zhang, Xin, and Jae-Hyun Lee. "Blockchain Technology for Cybersecurity Applications: A Literature Review." Journal of Information Security and Applications, vol. 52, 2020.
Paramasivan, A. (2024). Harnessing AI for behavioral insights: Unlocking the potential of transactional data. International Journal of Leading Research Publication (IJLRP), 5(10), 1–11.
Lipton, Zachary C. "The Mythos of Model Interpretability: In Machine Learning, the Concept of Interpretability Is Both Important and Slippery." 2016.
Bonneau, Joseph, et al. "Research Perspectives and Challenges for Bitcoin and Cryptocurrencies." Communications of the ACM, vol. 58, no. 12, 2015, pp. 104–12.
Doshi-Velez, Finale, and Been Kim. "Towards a Rigorous Science of Interpretable Machine Learning." arXiv Preprint arXiv:1702.08608, 2017.
Konstantinou, E., G. Panagiotakos, and G. Papageorgiou. "Blockchain and AI: A Perfect Match for the Cybersecurity Sector?" Journal of Cybersecurity and Privacy, vol. 1, no. 2, 2019, pp. 106–15.
Paramasivan, A. (2024). Robust and resilient: AI-based defense mechanisms in card transactions. International Journal of Leading Research Publication (IJLRP), 5(11), 1–10.
Singh, Shubham, Preeti Sharma, and Ritika Dahiya. "Security and Privacy Challenges in Blockchain: A Survey." Journal of King Saud University - Computer and Information Sciences, vol. 33, no. 1, 2021, pp. 1–16
Roscher, Roland, et al. "Explainable Machine Learning for Scientific Insights and Discoveries." IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 1, 2020, pp. 66–81.
Ali, Mujahid, et al. "Applications of Blockchain in IoT: A Comprehensive Survey." IEEE Communications Surveys & Tutorials, vol. 22, no. 2, 2020, pp. 1123–68.
Paramasivan, A. (2024). Predictive analytics in card transactions: How AI enhances fraud detection and prevention. International Journal of Innovative Research and Creative Technology, 10(6), 1–20.
