An improved cyber-attack detection and classification model for the internet of things systems using fine-tuned deep learning model

Dimensions

Leni, A. Ezil Sam and Anand, R. and Mythili, N. and Pugalenthi, R. (2025) An improved cyber-attack detection and classification model for the internet of things systems using fine-tuned deep learning model. International Journal of Sensor Networks, 47 (1). pp. 11-25. ISSN 1748-1279

Full text not available from this repository.

Abstract

This study proposes an enhanced cyber-attack detection model for IoT networks using deep learning. Feature selection was performed using a wrapper-based dwarf mongoose optimisation (W-DMO) algorithm. A hybrid triple attention BiLSTM model (TDeepBiL) classified attacks with high accuracy. The model achieved 99.44% accuracy on the UNSW-NB 15 dataset and 98.6% on the ToN-IoT dataset, outperforming existing methods, highlighting its effectiveness in improving IoT network security and attack detection.

Item Type: Article
Subjects: Computer Science > Computer Networks and Communications
Divisions: Medicine > Aarupadai Veedu Medical College and Hospital, Puducherry > Psychiatry
Depositing User: Unnamed user with email techsupport@mosys.org
Date Deposited: 25 Nov 2025 08:36
Last Modified: 25 Nov 2025 08:36
URI: https://vmuir.mosys.org/id/eprint/1044

Actions (login required)

View Item
View Item