Blockchain-Integrated AI Systems for Decentralized Cybersecurity

A Resilient Approach to Threat Detection

Authors

  • Dr. Sarah Thompson Assistant Professor, Department of Cybersecurity, University of Toronto, Toronto, Canada Author

Keywords:

Blockchain, Artificial Intelligence, Cybersecurity, Decentralization, Threat Detection, Distributed Networks

Abstract

The increasing frequency and sophistication of cyber threats necessitate innovative approaches to cybersecurity. This paper explores the integration of blockchain technology with artificial intelligence (AI) to develop decentralized cybersecurity systems. Such systems enhance resilience and trust in threat detection mechanisms across distributed networks. By leveraging blockchain's immutable ledger and AI's analytical capabilities, organizations can improve their ability to detect, respond to, and mitigate cyber threats. This research discusses the theoretical foundations of blockchain-integrated AI systems, examines their potential applications in cybersecurity, and highlights the benefits and challenges of implementing these technologies. The findings suggest that combining blockchain and AI can lead to more secure, efficient, and trustworthy cybersecurity solutions, ultimately fostering a proactive security posture in an increasingly digital world.

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Published

12-10-2024

How to Cite

[1]
Dr. Sarah Thompson, “Blockchain-Integrated AI Systems for Decentralized Cybersecurity: A Resilient Approach to Threat Detection”, Distrib Learn Broad Appl Sci Res, vol. 10, pp. 324–331, Oct. 2024, Accessed: Nov. 21, 2024. [Online]. Available: https://dlabi.org/index.php/journal/article/view/158

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