Abstract: DDoS attacks pose a significant threat to Software-Defined Networking (SDN) environments, often overwhelming traditional security mechanisms. This work is primarily concerned with designing an AI-driven DDoS detection and mitigation system, which is expected to improve scalability, adaptability, and overall efficiency of network security operations. The system aspires to use AI-based models, including Multi-Armed Bandit, Random Forest, and Online Gradient Boosting, to dynamically detect anomalies, classify attack traffic, and implement intelligent mitigation strategies in real time. A comparative analysis of these models illustrates the benefits of AI technologies in enhancing detection accuracy, reducing false positives, and optimizing network performance. The paper also provides an analysis of the challenges associated with AI-based intrusion prevention and explores various future directions, such as the use of federated learning for collaborative threat intelligence sharing. Through studies on AI-based cyber security solutions, many researchers recognize both the potential and challenges in the deployment of real-time, adaptive DDoS mitigation strategies.
Keywords: DDoS Mitigation, AI-Driven Security, SDN Protection, Multi-Armed Bandit, Online Gradient Boosting, Anomaly Detection, Threat Intelligence.
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DOI:
10.17148/IJARCCE.2025.144107
