Title: Malicious traffic segregation using hunger game jellyfish optimisation in IoT-cloud

Authors: Sunil Sonawane; Reshma Gulwani; Pooja Sharma

Addresses: Computer Engineering, D.Y. Patil University, Ambi, Talegaon Dabhade, Pune, Maharashtra, India ' Department of Information Technology, Dr. D.Y. Patil's Ramrao Adik Institute of Technology, Sector 7, Phase I, Pad. Dr. D.Y. Patil Vidyapeeth, Nerul, Navi Mumbai, Maharashtra, India ' Department of Computer Engineering, School of Engineering & Technology, D.Y. Patil University, Ambi, Talegaon Dabhade, Pune, Maharashtra, India

Abstract: The extensive growth rate in the Internet of Things (IoT) has acquired a huge focus on cybercriminals and the increasing count of cyber-attacks on IoT devices made it a complex process. Here, a new deep learning-assisted model is developed in cloud-IoT for malicious traffic segregation. Firstly, IoT cloud simulation is done and the data is routed to the Base Station (BS). Routing is implemented with Hunger Game Jellyfish Optimisation (HGJO), where the route is determined based on fitness function. Here, the malicious traffic segregation is executed at BS. The log files are supplied to data pre-processing and feature selection is implemented with Pearson correlation. The malicious traffic segregation is implemented using SpinalNet, which is trained using HGJO. The HGJO is produced by combining the Hunger Games Search (HGS) and Jellyfish Search (JS). The HGJO-SpinalNet provided an elevated accuracy of 91.4%, sensitivity of 91.2% and specificity of 92.1%.

Keywords: malicious traffic segregation; internet of things; IoT-cloud; Gaussian filter; Pearson correlation; SpinalNet.

DOI: 10.1504/IJGUC.2024.142749

International Journal of Grid and Utility Computing, 2024 Vol.15 No.6, pp.588 - 601

Received: 04 Jul 2023
Accepted: 28 Apr 2024
Published online: 20 Nov 2024 *

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