Privacy-preserving intrusion detection in Internet of medical things neural networks using a novel recurrent U-Net auto-encoder algorithm for biomedical applications
Affiliations: [a] Department of Computer Science and Engineering, Nehru Institute of Engineering and Technology, Coimbatore | [b] Department of CSE, SNS College of Technology, Coimbatore
Correspondence:
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Corresponding author. M. Ragul Vignesh, Assistant Professor, Department of Computer Science and Engineering, Nehru Institute of Engineering and Technology, Coimbatore E-mail: [email protected].
Abstract: The rapid development of Internet of Things (IoT) technology has enabled the emergence of the Internet of Medical Things (IoMT), especially in body area network applications. To protect sensitive medical data, it is essential to ensure privacy preservation and detect intrusions in this context. This study proposes a novel intrusion detection system that protects the privacy of IoMT networks, specifically in the context of body area networks. For feature extraction, the system employs a recurrent U-Net autoencoder algorithm, which effectively captures temporal dependencies in IoMT data. In addition, privacy is protected through the combination of data anonymization techniques and data classification using Principal Component Analysis (PCA). Combining the recurrent U-Net autoencoder algorithm, privacy preservation mechanisms, and PCA-based data classification, the proposed system architecture comprises the U-Net autoencoder algorithm. The proposed method is superior to existing approaches in terms of accuracy, precision, recall, F-measure, and classification loss, as demonstrated by experimental evaluations. This research contributes to the field of privacy protection and intrusion detection in IoMT, specifically in body area network applications.
Keywords: Biomedical, Internet of Medical Things, intrusion detection, privacy preservation, recurrent neural networks, U-Net
