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Intrusion Detection Framework for Industrial Wireless Sensor Networks in Smart Manufacturing
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Author(s): M.Nirmal Kumar (Bharath Institute of Higher Education and Research, India), T. Vijayan (Bharath Institute of Higher Education and Research, India)and B. Karthik (Bharath Institute of Higher Education and Research, India)
Copyright: 2026
Pages: 22
Source title:
Pioneering AI and Data Technologies for Next-Gen Security, IoT, and Smart Ecosystems
Source Author(s)/Editor(s): Ferdin Joe John Joseph (Thai-Nichi Institute of Technology, Thailand), Karthikeyan Chinnusamy (Veritas, USA), Joseph Jeganathan (University of Bahrain, Bahrain), Ahmed J. Obaid (University of Kufa, Iraq)and S. Suman Rajest (Dhaanish Ahmed College of Engineering, India)
DOI: 10.4018/979-8-3373-4672-4.ch010
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Abstract
IWSNs are necessary for smart manufacturing because they allow the continuous monitoring and control of resources. However, their susceptibility to cyber threats requires enhancing efficient and effective IDS systems. The proposed work introduces a newly developed approach based on ML and DL techniques to achieve efficient intrusion detection in IWSNs. The framework offers feature extraction using higher-order features and dominant Eigenspace to enhance the detection accuracy while maintaining enhanced computational time and selecting adaptive thresholds. The performance analysis against benchmark datasets shows a high level of effectiveness with a detection accuracy of 99.2%, precision of 97.9%, recall ratio of 98.3%, and F1 Score of 98.1%. Moreover, the experimental results show a decreased detection delay and improved resource efficiency, making the system ideal for real-time applications in smart manufacturing systems. However, the proposed framework is superior to the previous solutions in terms of accuracy and required time.
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