Title: An enhancing IOT security with a trinary deep learning paradigm and squirrel reptilian optimisation

Authors: K. Navaz; N. Muthuvairavan Pillai; G. Shanmuga Sundaram; T. Rajesh Kumar; C. Jehan

Addresses: School of Computing, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Chennai-600062, India ' Department of Computer Science and Business Systems, R.M.D. Engineering College, India ' CSE Department, Chennai Institute of Technology, Chennai-69, India ' Department of Computer Science and Engineering, Saveetha School of Engineering, SIMTS, Chennai, Tamil Nadu-602501, India ' Computer Science and Engineering Department, Chennai Institute of Technology, Chennai 69, India

Abstract: This research work proposes a novel approach for IoT intrusion detection using a trinary deep learning paradigm. The proposed model aims to address the challenges of large data requirements and false positives commonly encountered in IoT network security. The model consists of four phases: pre-processing, multi-modal feature extraction, optimal feature selection, and intrusion detection. Initially, the collected raw data is pre-processed using data cleaning techniques and Z-score normalisation, which helps to standardise the data for further analysis. Following pre-processing, multi-modal feature extraction techniques are applied, including measures of central tendency, database features, statistical dispersion, and information entropy-based features. To select the most relevant features from the extracted set, the squirrel reptilian optimisation algorithm is employed. SRO combines the squirrel search algorithm and reptile search algorithm to optimise feature selection, ensuring that only the most informative features are utilised for intrusion detection.

Keywords: IoT; intrusion detection; multi-modal feature extraction; squirrel reptilian optimisation algorithm; SRO; trinary-deep-learning-paradigm; CNN; RBFN; RBM.

DOI: 10.1504/IJMOR.2025.148076

International Journal of Mathematics in Operational Research, 2025 Vol.31 No.4, pp.544 - 575

Received: 28 Jul 2023
Accepted: 02 Aug 2023
Published online: 25 Aug 2025 *

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