Enhanced Abnormality Detection via PSO-Driven Adaptive Ensemble Weighting for Energy AIoT Device Security

Abstract

The present era is characterized by the interconnection, communication, connectivity, and data exchange of Internet of Things (IoT) devices. However, current systems often neglect to incorporate security protocols for IoT devices in the energy sector, the Internet of Medical Things, smart homes, and other areas. This poses a significant challenge in IoT networks as these devices possess constrained resources, making them vulnerable to attacks. Attackers can exploit these vulnerabilities to gain unauthorized access and retrieve sensitive information or data from the targeted devices. This paper presents a machine learning-driven intrusion detection system to tackle these issues, aiming to devise a system for early identification of such attacks. The system is tested using the WUSTL and UNSW-NB18 datasets to detect Man-in-the-Middle (MITM) and Botnet attacks. The developed system selects the optimal features from both datasets using Mutual Information (MI) and chi-square feature selection. It applies the Synthetic Minority Oversampling Technique (SMOTE) resampling method to resample the attack class and the Random Under Sampling method to resample the normal class. This study utilizes TabNet, Support Vector Machine (SVM), and Random Forest (RF) for both datasets. The performance is then compared with the proposed Ensemble Weighted Voting (EWV) classifier. The experimental results show that the proposed method PSO-EWV on the WUSTL dataset achieves 99.958% and 99.992% F-scores on the UNSW 2018 dataset for MITM attack and Botnet attack classification with MI feature selection. The experimental findings conclude that this method effectively detects attacks within an intrusion detection system.

Keywords

• Ensemble learning
• particle swarm optimization
• TabNet
• abnormal detection
• botnet attack
• MITM attack