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

Abstract

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.