Green Deep Reinforcement Learning for IoT Edge Intrusion Detection
The rapid expansion of the Internet of Things (IoT) has intensified cybersecurity challenges, particularly in detecting and mitigating Distributed Denial-of-Service (DDoS) attacks at the network edge. Traditional Intrusion Detection Systems (IDSs) remain limited by static signatures, dependence on labeled data, poor adaptability to evolving and zero-day attacks, and high computational overhead on resource-constrained edge gateways. Moreover, most Deep Reinforcement Learning (DRL)-based IDS studies prioritize detection performance while overlooking energy consumption and carbon impact. To address these limitations, this paper proposes two carbon-aware DRL-based IDS frameworks: DeepEdgeIDS, a label-free Autoencoder-DQN architecture for anomaly-guided online mitigation, and AutoDRL-IDS, a supervised LSTM-DQN model for temporally informed detection and response. Both systems incorporate multi-objective reward functions that jointly consider security performance, response latency, energy consumption, memory utilization, and estimated carbon emissions, using learning-paradigm-specific detection feedback. AutoDRL-IDS employs ground-truth-dependent detection metrics during supervised training, whereas DeepEdgeIDS relies on anomaly confidence and post-mitigation traffic stabilization for label-free online learning. The proposed systems are theoretically analyzed and experimentally evaluated on physical IoT edge gateways under DDoS traffic. The results show that AutoDRL-IDS achieves 94 percent detection accuracy, while DeepEdgeIDS attains 98 percent offline evaluation accuracy and demonstrates stronger adaptability to previously unseen attack patterns.