TL;DR
An IDS that integrates federated learning with Double Deep Q-Network (DDQN), a deep reinforcement learning algorithm, designed for large-scale MQTT-based IoT networks is proposed and validated for robustness against adversarial attacks and adaptability to evolving attack patterns.
This work proposes a novel multiclass intrusion detection system using FL at the subnetwork level using various machine learning models, including Artificial Neural Networks, Convolutional Neural Networks, and Long Short-Term Memory to build an effective IoT IDS.
The results show a success in implementing a real time, scalable, privacy-preserving, and adaptive IDS in large-scale IoT deployments through intelligent workload distribution between edge and cloud layers.
The review reveals that the most used algorithm for DRL-based IDS is Deep Q-Network (DQN), appearing in 8 studies (30.8%), and the most frequently targeted attacks are DoS, DDoS, Backdoors, Mirai, Reconnaissance, Scan, and Torii.
An offensive-defensive system based on Deep Reinforcement Learning (DRL) algorithms is proposed, which outperform several state-of-the-art machine learning approaches in the literature, revealing that the systematic incorporation of accurate data engineering and reinforcement learning frameworks generates a field-teste...
The framework introduces CNN–BiLSTM deep learning networks to represent traffic in a spatiotemporal manner and adopts ensemble machine learning classifiers to enhance the robustness of traffic detection and its interpretability, to enhance the robustness of traffic detection and its interpretability.
This research proposes a novel framework for anomaly detection in WSNs that leverages federated deep learning and prioritizes real-time adaptation and data privacy, and offers a promising path forward for securing WSNs by enabling distributed, privacy-preserving anomaly detection with real-time adaptation capabilities.
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