Feature Selection Model Based on Gorilla Troops Optimizer for Intrusion Detection Systems
Ibrahim Ahmed, Abdelghani Dahou, Samia Allaoua Chelloug, Mohammed A. A. Al‐qaness, Mohamed Abd Elaziz
Abstract
Cyber security is a fundamental challenge to the Internet of things (IoT) and smart home environments .This paper presents a modified method to ystem (IDS).setection dntrusion ienhance the performance of the This modification is achieved by introducing an alternative feature selection (FS) . ptimizer (GTO) algorithm.oroops torilla gmodel based on the Recently, FS has played a significant role in increasing the detection of anomalies in IDSs. To evaluate the efficiency of the developed method, a set of experimental conducted using three datasets, including NSL-KDD, CICIDS2017, and Bot-IoT datasets.asresults w xtraction (FE) model to reduce the dimensions of these datasets as a first step.Teeature f used as a areetworks (CNN) neural nonvolutional cThe hen, the extracted features are passed to the FS model for detection. The results of the developed method are compared with the well-known IDS technique. The results show the superiority of the developed method over all other methods according to the performance metrics.