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Original Article
Adaptive AI Framework for Anomaly Detection and DDoS Mitigation in Distributed Systems
Karthik Kamarapu1
Kali Rama Krishna Vucha2
1Independent Software Researcher, Osmania University, Hyderabad, Telangana, India. 2Independent Software Researcher, Acharya Nagarjuna University, Guntur, Andhra Pradesh, India.
Published Online: January-April 2025
Pages: 23-31
Cite this article
↗ https://www.doi.org/10.59256/indjcst.20250401005References
1. N. Mohamed, "DDoS Attacks Mitigation: A Review of AI-Based Strategies and Techniques," in IEEE Communications Surveys & Tutorials,
2024. [Online]. Available: https://ieeexplore.ieee.org
2. B. Bala and S. Behal, "AI techniques for IoT-based DDoS attack detection: Taxonomies, comprehensive review and research challenges,"
in Journal of Network and Computer Applications, 2024. [Online]. Available: https://www.sciencedirect.com
3. S. Ahmadi, "AI in the Detection and Prevention of Distributed Denial of Service (DDoS) Attacks," in International Conference on
Cybersecurity Research and Innovation, 2024. [Online]. Available: https://hal.science
4. N. Moustafa, "A new distributed architecture for evaluating AI-based security systems at the edge: Network TON_IoT datasets," in Future
Generation Computer Systems, 2021. [Online]. Available: https://www.sciencedirect.com
5. C. S. Kalutharage, X. Liu, C. Chrysoulas, and N. Pitropakis, "Explainable AI-based DDoS attack identification method for IoT networks,"
in Computers, 2023. [Online]. Available: https://www.mdpi.com
6. S. Kumar, M. Dwivedi, M. Kumar, and S. S. Gill, "A comprehensive review of vulnerabilities and AI-enabled defense against DDoS attacks
for securing cloud services," in Journal of Information Security and Applications, 2024. [Online]. Available: https://www.sciencedirect.com
7. S. A. Varma and K. Ganesh Reddy, "An AI-based IDS framework for detecting DDoS attacks in cloud environment," in International Journal
of Computer Networks & Communications, 2024. [Online]. Available: https://www.tandfonline.com
8. O. Polat, S. Oyucu, M. Türkoğlu, H. Polat, and A. Aksoz, "Hybrid AI-Powered Real-Time Distributed Denial of Service Detection and Traffic
Monitoring for Software-Defined-Based Vehicular Ad Hoc Networks," in Applied Sciences, 2024. [Online]. Available: https://www.mdpi.com
2024. [Online]. Available: https://ieeexplore.ieee.org
2. B. Bala and S. Behal, "AI techniques for IoT-based DDoS attack detection: Taxonomies, comprehensive review and research challenges,"
in Journal of Network and Computer Applications, 2024. [Online]. Available: https://www.sciencedirect.com
3. S. Ahmadi, "AI in the Detection and Prevention of Distributed Denial of Service (DDoS) Attacks," in International Conference on
Cybersecurity Research and Innovation, 2024. [Online]. Available: https://hal.science
4. N. Moustafa, "A new distributed architecture for evaluating AI-based security systems at the edge: Network TON_IoT datasets," in Future
Generation Computer Systems, 2021. [Online]. Available: https://www.sciencedirect.com
5. C. S. Kalutharage, X. Liu, C. Chrysoulas, and N. Pitropakis, "Explainable AI-based DDoS attack identification method for IoT networks,"
in Computers, 2023. [Online]. Available: https://www.mdpi.com
6. S. Kumar, M. Dwivedi, M. Kumar, and S. S. Gill, "A comprehensive review of vulnerabilities and AI-enabled defense against DDoS attacks
for securing cloud services," in Journal of Information Security and Applications, 2024. [Online]. Available: https://www.sciencedirect.com
7. S. A. Varma and K. Ganesh Reddy, "An AI-based IDS framework for detecting DDoS attacks in cloud environment," in International Journal
of Computer Networks & Communications, 2024. [Online]. Available: https://www.tandfonline.com
8. O. Polat, S. Oyucu, M. Türkoğlu, H. Polat, and A. Aksoz, "Hybrid AI-Powered Real-Time Distributed Denial of Service Detection and Traffic
Monitoring for Software-Defined-Based Vehicular Ad Hoc Networks," in Applied Sciences, 2024. [Online]. Available: https://www.mdpi.com
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