AI Driven Context-Aware DDoS Detection and Mitigation Framework Using Optimized CNN–BiLSTM and Reinforcement Learning

Abstract

The exponential growth of interconnected systems across the Internet of Things (IoT), Software-Defined Networks (SDN), and cloud environments has led to a drastic increase in the scale and advancement of Distributed Denial of Service (DDoS) attacks. Conventional defences based on machine learning are often static, traffic-centric, and lack adaptivity to dynamic network behaviour, resulting in high false-positive rates and delay in mitigation responses. To address these challenges, this paper presents a conceptual framework for an AI-driven, context-aware DDoS detection and mitigation system. The proposed approach employs a optimized Convolutional Neural Network–Bidirectional Long Short-Term Memory (CNN–BiLSTM) model for efficient, real-time detection of DDoS attacks. The model captures spatial and temporal correlations in traffic while incorporating contextual parameters such as Quality of Service (QoS), Service Level Agreement (SLA) priority, and workload telemetry. These contextual attributes enable the model to distinguish legitimate network fluctuations from malicious activities, so that the false positive rate will be reduced without compromising accuracy. For adaptive response, a Reinforcement Learning (RL)-based mitigation layer—exploiting Deep Q-Network (DQN) and Actor–Critic (AC) variants—continuously learns optimal mitigation actions based on real-time feedback. The interaction between detection and mitigation components forms a context-aware feedback loop, enabling dynamic policy refinement and continuous system improvement. Comprehensive literature analysis reveals that existing AI-based DDoS defense mechanisms lack integration between detection and mitigation, and often overlook contextual intelligence. The proposed framework bridges this gap by introducing a self-adaptive, feedback-driven defense system capable of maintaining detection precision, low latency, and QoS preservation in heterogeneous environments.