Development of Intelligent Intrusion Detection System for Smart Water Treatment and Distribution Plants in IoT-Enabled Critical Infrastructures

Abstract

This thesis aims to address the critical issue of detecting intrusions in IoT-enabled critical infrastructure, with a specific focus on water treatment and distribution plants. The research employs Random forest, SVM and Neural Networks, to identify the optimal solution for intrusion detection. The models were trained and tested using a large-scale time series dataset comprising approximately 400k records. Intrusions were identified based on the time series data, and the stage of each attack on the test bed was determined. The results indicate that bucket approach used for training the models was highly effective compared to direct feed and successfully detects the vast majority of the attacks with a low false positive rate thus improving on previous works based on this data set. Amongst all the models compared, neural network outperforms other models in terms of accuracy and evaluation metrics while using the bucket approach. This study has the potential to contribute significantly to the field of cyber security for critical infrastructure systems, particularly for water treatment and distribution plants.