Advanced Machine Learning and NLP Strategies for Robust DDoS Attack Detection: A Comprehensive Analysis
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N\AAbstract
Distributed Denial of Service (DDoS) attacks threaten network availability in critical systems like IoT and cloud infrastructure. This paper presents an in- depth analysis of advanced machine learning (ML) and natural language processing (NLP) strategies, including Graph Neural Networks (GNNs) and Deep Reinforce- ment Learning (DRL), for robust DDoS detection. Experiments leverage trans- fer learning, federated learning, anomaly detection, and explainable AI, validated with CICDDos2019, synthetic logs, and NS-3/Mininet simulations, achieving up to 98.37% accuracy. Six charts and six tables, alongside ten mathematical for- mulations, elucidate model performance, feature importance, and scalability. We address feature selection, preprocessing, adversarial robustness, and deployment challenges, offering novel insights from 30 peer-reviewed sources.
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