Decentralized AI-Based Intrusion Detection for Zero-Day Attacks in Cloud Networks
DOI:
https://doi.org/10.63345/pnxad587Keywords:
Decentralized AI, Intrusion Detection System, Zero-Day Attacks, Cloud Security, Federated Learning, Blockchain-Based IDS, Cyber Threat IntelligenceAbstract
As cloud networks continue to expand, cybersecurity threats, particularly zero-day attacks, pose a significant risk. Traditional Intrusion Detection Systems (IDS) often struggle to detect and mitigate unknown vulnerabilities due to their reliance on pre-existing attack signatures. This paper proposes a decentralized AI-based Intrusion Detection System (DAI-IDS) designed to counteract zero-day attacks in cloud environments. By integrating federated learning, blockchain technology, and deep learning-based anomaly detection, the proposed system ensures real-time threat identification while preserving data privacy. Empirical analysis demonstrates that DAIIDS outperforms centralized IDS models in terms of detection accuracy, response time, and resilience to adversarial attacks.
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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Articles are published under the Creative Commons Attribution NonCommercial 4.0 License (CC BY NC 4.0), allowing others to distribute, remix, adapt, and build upon the work for non-commercial purposes while crediting the original author.
