Distributed optimization for IoT attack detection using federated learning and Siberian Tiger optimizer

Distributed optimization for IoT attack detection using federated learning and Siberian Tiger optimizer

The rapid growth of IoT devices has heightened the risk of botnet attacks, calling for scalable and distributed detection solutions. In this context, this study proposes a distributed optimization system for IoT attack detection using CNN model utilizing federated learning. After optimizing the hype...

Full description

Saved in:
Bibliographic Details
Main Authors: Brij B. Gupta, Akshat Gaurav, Wadee Alhalabi, Varsha Arya, Eman Alharbi, Kwok Tai Chui
Format: Article
Language:English
Published: Elsevier 2025-06-01
Series:ICT Express
Subjects:
Federated learning
Distributed optimization
IoT attack detection
Siberian Tiger Optimization (STO)
CNN model
Online Access:http://www.sciencedirect.com/science/article/pii/S2405959525000281
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The rapid growth of IoT devices has heightened the risk of botnet attacks, calling for scalable and distributed detection solutions. In this context, this study proposes a distributed optimization system for IoT attack detection using CNN model utilizing federated learning. After optimizing the hyperparameters of the model at the server, the Siberian Tiger Optimization (STO) method distributes these values to clients for dispersed training. Our model achieves accuracy, recall, and precision of 0.89978, 0.94355, and 0.94455, respectively, using the N-BaIoT dataset. These findings show, in spite of latency issues, the efficiency of federated learning in distributed IoT security systems.
ISSN:2405-9595

Similar Items