Interdisciplinary framework for cyber-attacks and anomaly detection in industrial control systems using deep learning.

The reliability of Industrial Control Systems (ICS) is crucial given their substantial importance in society and business. These systems are susceptible to physical and cyber-attacks, which can seriously affect human life and the economy. Given the growing prevalence of Internet of Things (IoT) technologies and the possibility of cyber warfare, it is essential to prioritize safeguarding Industrial Control Systems (ICS) from cyberattacks. Previous studies revealed an imbalance in the ICS datasets. As a result, models didn't do well in minority classes but did well in majority classes, which made the intrusion detection system (IDS) less useful. The key objective is to provide insights into the normal functioning of the system and the disruptions produced by cyberattacks. In this study, we introduced an interdisciplinary framework that aims to enhance network intrusion detection systems (NIDSs). In this framework, we introduced an IDS via feature selection and feature reduction technique(s) with the attention-driven lightweight deep neural networks: Deep Recurrent Neural Networks (RNN), Deep Long Short-Term Memory (LSTM), and Deep Bi-directional Long Short-Term Memory (Bi-LSTM). Several feature selection techniques exclude features that fail to match the specified criteria. We employed Sparse Principal Component Analysis (SPCA) to extract higher-order features. We conducted experiments on the three datasets: the Secure Water Treatment System (SWaT), the Water Distribution (WADI), and the Gas Heating Loop (GHL). Among the models used in the framework, the attention-driven Deep LSTM model produced better results than the others, revealing lower training and testing times for the three datasets. In terms of precision, recall, F1-score, computational speed, and ability to work with larger datasets and different ICSs, the proposed framework is better than previous methods in detecting cyberattacks. This highlights how useful it is in the real world.