FRAMEWORK FOR REAL-TIME ATTACK PREDICTION AND LEGITIMATE TRAFFIC PROTECTION
Keywords:
Cognitive DDoS Defense Fabric, AI-Legitimate Traffic Protection, Predictive Cognitive Defense, Reinforcement Learning, Federated Attack Intelligence Exchange, Autonomous Cyber Defense, Network Resilience, Low-Latency Security, Self-Learning Mitigation SystemsAbstract
Modern DDoS mitigation systems remain predominantly reactive, relying on threshold-basedtriggers and preconfigured rules that struggle to keep pace with AI-driven and polymorphicattack vectors. In high-frequency and low-latency environments such as financial trading infrastructures, these conventional systems
References
Anderson, M., Chen, L. and Rodriguez, P. (2024) 'Adversarial machine learning techniques for evading DDoS detection systems', IEEE Transactions on Information Forensics and Security, 19(4), pp. 892-907.
Chen, W. and Liu, Y. (2023) 'Ensemble learning approaches for improved DDoS attack detection', Computer Networks, 215, pp. 109-124
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Published
2024-05-15
How to Cite
Suresh Kumar Balakrishnan. (2024). FRAMEWORK FOR REAL-TIME ATTACK PREDICTION AND LEGITIMATE TRAFFIC PROTECTION . Journal of Computational Analysis and Applications (JoCAAA), 33(05), 2918–2943. Retrieved from https://www.eudoxuspress.com/index.php/pub/article/view/3885
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