A Study of Contemporary ML Algorithm on Intrusion Detection Using IIoT Datasets

The global digitization of most activities of corporate and non-corporate bodies has come to stay. The interconnection of physical object "things" embedded with different technologies such as sensors, softwares and other technologies to connect and allow for data exchange with different systems and devices over the internet is known as the Internet of Things (IoT). To guarantee lower expenses and increased operational effectiveness, Data-driven insights for prompt decision-making, complete, remote asset and resource management, Prescriptive, predictive, and real-time insights along with enhanced end-user satisfaction Industrial Internet of Things (IIoT) became a given. Implementing machine learning (ML) algorithms for intrusion detection in IIoT systems entails numerous hurdles, such as data imbalance and adversarial attacks. And different ML algorithms handle data imbalance and adversarial attacks for intrusion detection differently. Therefore, there is a need for comparative studies on ML algorithms to be used as a tool to help prevent unauthorised intrusion into various networks. In this research, an experimental approach has been used to compare contemporary ML algorithms such as Linear SVC, random forest RF, XGBoost, decision tree (DT), logistic regression (LR) on IIoT datasets to detect intrusion. Data exploration, Feature engineering, selection and data partition were done at a rate of 80-20 % ratio for the train set and test set respectively. Performance evaluation was carried out using accuracy, recall, precision and F1-score and a confusion matrix was plotted. The performance evaluation shows the accuracy of 100%, 99.99%, 99.35%, 97.49% and 97.27% for RF, XGBoost, DT, Linear SVC and LR algorithms respectively the test set evaluation. RF outperformed the other algorithm that was experimented.