Enhancing trustworthiness among iot network nodes with ensemble deep learning-based cyber attack detection

Abstract

A lot of machine learning methods and expert systems are used in network intrusion detection automation. When different industrial control systems merge with the Internet of Things (IoT) environment, they become vulnerable to cyber-attacks in critical infrastructure situations requiring communication technologies. Conventional machine learning techniques used for network anomaly detection are ineffective due to the substantial amount of network traffic within important Cyber-Physical Systems (CPSs). In this manuscript, Cyberattack Identification Through Ensemble Deep Learning in an IoT environment is proposed. Initially, the input network traffic data are taken from the IoT-23 dataset. Then the network traffic data are preprocessed using Z-Score normalization to reduce any irrelevant or erroneous data from the input dataset. Then the relevant features are selected using the Gorilla Troops Optimization (GTO) algorithm. Afterwards, the selected features are fed into the ensemble classification model based on Random Space (RS), Random Tree (RT), Extreme Gradient Boosting (XGBoost), and Graph Convolutional Neural Network (GCNN). Among the several ensembling techniques, GCNN can achieve better performance. Python is used to accomplish the suggested technique. The performance of the proposed GCNN method provides 12.09%, 4.34%, and 3.21% higher accuracy than the other models like RS, RT and XGBoost respectively.