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A Hybrid Deep Learning Framework for Enhanced Intrusion Detection in IoT Networks: Leveraging Feature Engineering and Attention Mechanisms

Authors: Rachna Sharma

Published: 2025-05-02 11:41:01.387

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Abstract

The Internet of Things (IoT) is rapidly expanding, connecting billions of devices and transforming various aspects of our lives. However, this interconnectedness introduces significant security challenges, making IoT networks vulnerable to various cyberattacks. Traditional security measures are often inadequate to address the complex and evolving threat landscape. This paper proposes a hybrid deep learning framework for enhanced intrusion detection in IoT networks. The framework integrates feature engineering techniques to extract relevant and informative features from network traffic data with a deep learning model incorporating attention mechanisms. The attention mechanism allows the model to focus on the most critical features for accurate intrusion detection. Experimental results on a publicly available IoT intrusion detection dataset demonstrate the effectiveness of the proposed framework in achieving high detection accuracy and low false alarm rates, outperforming existing state-of-the-art methods. The framework offers a robust and adaptable solution for securing IoT networks against evolving cyber threats.

Keywords

Intrusion Detection System (IDS), Internet of Things (IoT), Deep Learning, Feature Engineering, Attention Mechanism, Network Security, Cybersecurity, Hybrid Model, Anomaly Detection, Machine Learning

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How to Cite

Rachna Sharma, (2025-05-02 11:41:01.387). A Hybrid Deep Learning Framework for Enhanced Intrusion Detection in IoT Networks: Leveraging Feature Engineering and Attention Mechanisms. JANOLI International Journal of Artificial Intelligence and its Applications, Volume EOCMPeqBj5R9ZDur0Rlk, Issue 4.

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