Securing the Edge: CatBoost Classifier Optimized by the Lyrebird Algorithm to Detect Denial of Service Attacks in Internet of Things-Based Wireless Sensor Networks

Future Internet, Journal Year: 2024, Volume and Issue: 16(10), P. 381 - 381

Published: Oct. 19, 2024

The security of Wireless Sensor Networks (WSNs) is the utmost importance because their widespread use in various applications. Protecting WSNs from harmful activity a vital function intrusion detection systems (IDSs). An innovative approach to WSN (ID) utilizing CatBoost classifier (Cb-C) and Lyrebird Optimization Algorithm presented this work (LOA). As typical ID settings, Cb-C excels at handling datasets that are imbalanced. lyrebird’s remarkable capacity imitate sounds its surroundings served as inspiration for LOA, metaheuristic optimization algorithm. WSN-DS dataset, acquired Prince Sultan University Saudi Arabia, used assess suggested method. Among models presented, LOA-Cb-C produces highest accuracy 99.66%; nevertheless, when compared with other methods discussed article, error value 0.34% lowest. Experimental results reveal strategy improves WSN-IoT over existing terms false alarm rate.

Language: Английский

Intrusion Detection Systems for the Internet of Things Network: Survey on Rare Attacks

Published: July 23, 2024

Language: Английский