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

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YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.5755/j02.eie.40003

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.

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Type: article
Language: en
Landing Page: https://doi.org/10.5755/j02.eie.40003
PDF: https://eejournal.ktu.lt/index.php/elt/article/download/40003/17135
OA Status: gold
OpenAlex ID: https://openalex.org/W4415052806

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DOI: https://doi.org/10.5755/j02.eie.40003

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

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Type: article

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Language: en

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Publication year: 2025

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Publication date: 2025-06-27

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Authors: Mohamed Abbas, Mohammed I. Al‐Rayif

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Landing page: https://doi.org/10.5755/j02.eie.40003

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PDF URL: https://eejournal.ktu.lt/index.php/elt/article/download/40003/17135

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