A novel hybrid ensemble learning for anomaly detection in industrial sensor networks and SCADA systems for smart city infrastructures

Abstract

Critical Infrastructures (CIs) use Supervisory Control and Data Acquisition (SCADA) systems for monitoring and remote control. Sensor networks are being integrated into all areas of the infrastructures of smart cities. The sensor network data stream contains information that can be utilized to model and control the activity of these infrastructures. However, SCADA systems are constantly exposed to a variety of diverse intrusions, making detection with traditional intrusion detection systems (IDS) extremely difficult. Due to their unique specifications, conventional security solutions, like antivirus and firewall software, are unsuitable for properly securing SCADA systems. In addition, anomaly detection in industrial sensor networks (ISNs) should occur in real time. Therefore, effectively identifying cyberattacks in major SCADA systems is unquestionably essential for enhancing their resilience, ensuring safe operations, and avoiding expensive maintenance. We developed a novel hybrid ensemble model approach to address these issues. This paper's primary objective is to detect hostile intrusions that have already circumvented firewalls and typical IDS. In this paper, we propose a hybrid Ensemble Learning Model (ELM) for intrusion detection in SCADA systems with ISNs utilizing a tangible data gathered from a gas pipeline system given by Mississippi State University (MSU), the water system, and the high-dimensional University of New South Wales-NB 2015 (UNSW-NB15) data that reflects a typical attack in the Internet of Things (IoT) environment. The unity normalization method was adopted for data preprocessing, and the Principal Component Analysis (PCA) was utilized for feature extraction of the high-dimensional datasets. Grey Wolf Optimizer (GWO) was used for optimizing the bagging, stacking, Adaboost, and an ensemble of classifiers Naive Bayes and Support Vector Machine with a majority voting technique. Then, we utilized the proposed approach founded on the bijective soft-set approach for efficient ELM selection. The experiment was conducted in two phases: Initially, without PCA + GWO for feature extraction and selection on the ELM, and subsequently, with PCA + GWO for feature extraction and selection on the ELM. PCA + GWO on the ensemble of classifiers NB + SVM provided an accuracy of 99%, precision of 100%, recall of 100%, and detection rate of 99.90%, outpacing the ensemble of classifiers without PCA feature extraction and GWO optimization approaches.