Title: Enhancing IoMT security: spotted hyena optimised bi-layered attention adaptive recurrent network for intrusion detection

Authors: Smiley Gandhi; Santosh Kumar; T. Poongodi; K. Sampath Kumar

Addresses: Department of Computer Science and Engineering, Galgotias University, Greater Noida-203201, India ' Department of Computer Science and Engineering, Galgotias University, Greater Noida-203201, Uttar Pradesh, India ' Department of Computer Science and Engineering, School of Engineering, Dayananda Sagar University, Bangalore-560078, Karnataka, India ' Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences (SIMATS), Saveetha University, Chennai, Tamil Nadu, 602105, India

Abstract: Intrusion detection is essential to computer and network security because it detects unauthorised or malicious activity. It monitors and analyses network or system activity in real-time to alert or act on suspicious. Internet of medical things (IoMT) uses intrusion detection and attack detection systems to protect medical devices, healthcare systems, and patient data from cyberattacks. Researcher introduces a new IoMT-supported spotted hyena optimised bi-layered attention adaptive recurrent network (SHO-BAARNN) for intrusion detection. The spotted hyena optimisation (SHO) is used to adjust model parameters for better robustness and efficiency when processing IoMT data. The adaptive recurrent network enables real-time intrusion detection, while the BAARNN structure offers dual attention techniques to selectively focus on important data aspects. The incorporation of optimisation, attention mechanisms, and recurrent networks, among other practical implications, makes it an effective tool for real-time detection of intrusions, providing improved security for sensitive healthcare data and medical equipment.

Keywords: intrusion detection; internet of medical things; IoMT; packet level features; correlation-based feature selection; spotted hyena optimised bi-layered attention adaptive recurrent network; SHO-BAARNN; real-time data streams.

DOI: 10.1504/IJCCBS.2026.153757

International Journal of Critical Computer-Based Systems, 2026 Vol.12 No.1, pp.1 - 20

Received: 13 Jun 2024
Accepted: 21 Apr 2025
Published online: 26 May 2026 *

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