Advanced AI-driven intrusion detection for securing cloud-based industrial IoT

IF 5 3区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Egyptian Informatics Journal Pub Date : 2025-04-04 DOI:10.1016/j.eij.2025.100644

Saima Siraj Qureshi , Jingsha He , Siraj Uddin Qureshi , Nafei Zhu , Ahsan Wajahat , Ahsan Nazir , Faheem Ullah , Abdul Wadud

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引用次数: 0

Abstract

The rapid integration of smart devices with cloud services in the Industrial Internet of Things (IIoT) has exposed significant vulnerabilities in conventional security protocols, making them insufficient against sophisticated cyber threats. Despite advancements in intrusion detection systems (IDS), there remains a critical need for highly accurate, adaptive, and scalable solutions for cloud-based IIoT environments. Motivated by this necessity, we propose an advanced AI-powered IDS leveraging Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks. Developed using Python and the Kitsune dataset, our IDS demonstrates a remarkable detection accuracy of 98.68%, a low False Negative rate of 0.01%, and an impressive F1 score of 98.62%. Comparative analysis with other deep learning models validates the superior performance of our approach. This research contributes significantly to enhancing cybersecurity in cloud-based IIoT systems, offering a robust, scalable solution to mitigate evolving cyber threats.

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来源期刊

Egyptian Informatics Journal Decision Sciences-Management Science and Operations Research

CiteScore

11.10

自引率

1.90%

发文量

审稿时长

110 days

期刊介绍： The Egyptian Informatics Journal is published by the Faculty of Computers and Artificial Intelligence, Cairo University. This Journal provides a forum for the state-of-the-art research and development in the fields of computing, including computer sciences, information technologies, information systems, operations research and decision support. Innovative and not-previously-published work in subjects covered by the Journal is encouraged to be submitted, whether from academic, research or commercial sources.