Enhancing Intrusion Detection and Mitigation in Ad Hoc Networks Using an AI-Driven Deep Learning Approach

Ad hoc networks are increasingly deployed in critical applications due to their flexibility and scalability. However, their decentralised and dynamic nature makes them highly vulnerable to a range of sophisticated security threats. This paper aims to improve the efficiency of intrusion detection and mitigation in ad hoc networks using an AI-driven deep learning approach. A hybrid deep learning model is proposed, integrating convolutional neural networks (CNNs) for feature extraction and long short-term memory networks (LSTMs) for temporal analysis to effectively detect malicious activities. Reinforcement learning, particularly using a deep Q-network (DQN), is applied to dynamically select optimal mitigation strategies. Federated learning is also used to train the model in a distributed manner, ensuring privacy while allowing scalability across network nodes. The proposed approach shows significant improvements in intrusion detection accuracy, exceeding 90 %, and offers effective real-time mitigation strategies. These results provide a comprehensive and adaptive framework for securing ad hoc networks against evolving threats.