Title: Attention segmental recurrent neural network optimised with sheep flock optimisation-based intrusion detection framework for securing IoT

Authors: M. Ramkumar Raja; P.J. Sathish Kumar; Jayaraj Velusamy; Krishnan Somasundaram

Addresses: Department of Electrical Engineering, College of Engineering, King Khalid University, Abha, KSA ' Department of Computing Science and Engineering, Panimalar Engineering College, Chennai, Tamil Nadu, India ' Department of Electronics and Communication Engineering, Nehru Institute of Engineering and Technology, Nehru Gardens, Thirumalayampalayam, Coimbatore – 641105, Tamil Nadu, India ' Sri Muthukumaran Institute of Technology, Chikkarayapuram, Near Mangadu, Chennai – 600069, Tamil Nadu, India

Abstract: This manuscript proposes an attention segmental recurrent neural network (ASRNN) optimised with sheep flock optimisation based intrusion detection scheme for securing internet of things (IoT) environment. Initially, the data is fed to pre-processing, wherein, the redundancy eradication and missing value replacements are performed by random forest and local least squares (LLS). Afterward, pre-processing data is supplied to the feature selection to select optimal features. The correlation feature selection-based processing of feature selection is done. The selected features are fed to attention segmental recurrent neural network, which categorises the data as normal or anomalies. Finally, sheep flock optimisation (SFO) is considered to optimise the ASRNN. The simulation performance of the proposed technique attains better accuracy 20.56%, 18.67%, 23.77%, 38.45%, 22.75%, 36.45%, higher precision 42.36%, 22.15%, 56.45%, 22.03%, 28.63%, and 21.36% compared with the existing methods.

Keywords: wireless networks; feature selection; sheep flock optimisation; SFO; attention segmental recurrent neural network; ASRNN; intrusion detection systems; IDS.

DOI: 10.1504/IJBIC.2025.146393

International Journal of Bio-Inspired Computation, 2025 Vol.25 No.3, pp.156 - 167

Received: 28 Feb 2023
Accepted: 19 Mar 2024
Published online: 28 May 2025 *

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