Hybrid Attention-Guided Deep Learning Framework for Enhanced Intrusion Detection in IoT Networks

Gnanzou, D.

Hybrid Attention-Guided Deep Learning Framework for Enhanced Intrusion Detection in IoT Networks

Authors: Gnanzou, D.

Published: 2025-04-28 19:22:28.129

Abstract

The proliferation of Internet of Things (IoT) devices has created a vast attack surface, making IoT networks increasingly vulnerable to various cyber threats. Traditional intrusion detection systems (IDS) often struggle to effectively identify complex and evolving attack patterns in these dynamic environments. This paper proposes a novel hybrid attention-guided deep learning framework for enhanced intrusion detection in IoT networks. The framework integrates convolutional neural networks (CNNs) for feature extraction, recurrent neural networks (RNNs) with attention mechanisms for temporal dependency modeling, and a deep neural network (DNN) for classification. The attention mechanism allows the model to focus on the most relevant features during the detection process, improving accuracy and reducing false positives. The performance of the proposed framework is evaluated using the NSL-KDD dataset, demonstrating its superiority over existing state-of-the-art IDS approaches in terms of detection accuracy, precision, recall, and F1-score. The results highlight the effectiveness of the hybrid attention-guided deep learning model in securing IoT networks against sophisticated cyberattacks.

Keywords

Intrusion Detection Systems (IDS), IoT Security, Deep Learning, Attention Mechanisms, Hybrid Models, Network Security, Anomaly Detection, Cybersecurity, Feature Extraction, NSL-KDD Dataset

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How to Cite

Gnanzou, D., (2025-04-28 19:22:28.129). Hybrid Attention-Guided Deep Learning Framework for Enhanced Intrusion Detection in IoT Networks. JANOLI International Journal of Artificial Intelligence and its Applications, Volume EOCMPeqBj5R9ZDur0Rlk, Issue 2.

ISSN: 3048-6815

JANOLI International Journal of
Artificial Intelligence and its Applications ( JIJAIA )