Title: Hybrid optimised deep residual network with trust parameters for intrusion detection in IoT

Authors: Asha Rawat; Harsh Namdev Bhor; Jayprabha Terdale; Varsha Bhole; Anuradha Thakare; Vishal Ratansing Patil

Addresses: School of Technology, SVKM's Narsee Monjee Institute of Management Studies (NMIMS), Navi Mumbai, Maharashtra, India ' Department of Information Technology, K.J. Somaiya Institute of Technology, Sion, Mumbai, Maharashtra, India ' Department of Electronics and Telecommunication Engineering, A.C. Patil College of Engineering Kharghar, Navi Mumbai, Maharashtra, India ' Department of Electronics and Telecommunication Engineering, A.C. Patil College of Engineering Kharghar, Navi Mumbai, Maharashtra, India ' Department of Computer Science and Engineering (AIML), Pimpri Chinchwad College of Engineering Nigdi, Pune, Maharashtra, India ' Department of Computer Science and Engineering (AIML), Vishwakarma Institute of Technology, Pune, Maharashtra, India

Abstract: Security issues are still challenging due to the availability of brilliant skills and hacking tools. Thus, detecting the intrusion in the IoT environment is crucial. Hence, this research introduces a novel optimised deep residual network based on the trust and KDD parameters. Here, an efficient mayfly spider monkey optimisation (MSMO) is proposed for tuning the adjustable parameters of the intrusion detector named deep residual network (DRN), which is modelled by hybridising the social behaviour of the mayfly in the mayfly optimisation algorithm (MA) with the foraging behaviour of the spider monkey based on the fission property of the spider monkey optimisation (SMO) to obtain the global best solution. Here, the trust factors and the KDD Cup features are considered for learning the classifier. The proposed model obtained better performance in accuracy of 0.913, precision of 0.919, false alarm rate of 0.084, and recall of 0.958.

Keywords: intrusion detection; deep residual networks; optimisation; trust factors; KDD Cup features.

DOI: 10.1504/IJIIDS.2026.150434

International Journal of Intelligent Information and Database Systems, 2026 Vol.18 No.1, pp.1 - 29

Received: 17 Nov 2023
Accepted: 10 Sep 2024
Published online: 13 Dec 2025 *

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