Industrial control systems (ICSs) play a crucial role in managing and monitoring critical processes across various industries, such as manufacturing, energy, and water treatment. The connection of equipment from various manufacturers, complex communication methods, and the need for the continuity of operations in a limited environment make it difficult to detect system anomalies. Traditional approaches that rely on supervised machine learning require time and expertise due to the need for labeled datasets. This study suggests an alternative approach to identifying anomalous behavior within ICSs by means of unsupervised machine learning. The approach employs unsupervised machine learning to identify anomalous behavior within ICSs. This study shows that unsupervised learning algorithms can effectively detect and classify anomalous behavior without the need for pre-labeled data using a composite autoencoder model. Based on a dataset that utilizes HIL-augmented ICSs (HAIs), this study shows that the model is capable of accurately identifying important data characteristics and detecting anomalous patterns related to both value and time. Intentional error data injection experiments could potentially be used to validate the model’s robustness in real-time monitoring and industrial process performance optimization. As a result, this approach can improve system reliability and operational efficiency, which can establish a foundation for safe and sustainable ICS operations.

Industrial control systems (ICS) are vital for ensuring the reliability and operational efficiency of critical infrastructure across various industries. However, due to their integration into modernized network environments, they are inadvertently exposed to a variety of cybersecurity threats that can compromise the reliability of critical infrastructure. This study aims to enhance ICS security by introducing a Zero Inflated Poisson (ZIP) based GRU Learning model to detect anomalies of ICS traffic in conjunction with the MITRE ATT&CK framework. The model’s effectiveness was validated through experiments simulating two major cyberattack scenarios: the ‘Stuxnet’ attack and the ‘Industroyer’ attack, achieving over 95% success in attack detection. By mapping the anomalies to the MITRE ATT&CK framework, we were able to lay the groundwork for an efficient response strategy to the attacks. These findings are expected to make a meaningful contribution to assessing and strengthening the security posture of ICS.

This study proposes a deep learning model utilizing the BACnet (Building Automation and Control Network) protocol for the real-time detection of mechanical faults and security vulnerabilities in building automation systems. Integrating various machine learning algorithms and outlier detection techniques, this model is capable of monitoring and learning anomaly patterns in real-time. The primary aim of this paper is to enhance the reliability and efficiency of buildings and industrial facilities, offering solutions applicable across diverse industries such as manufacturing, energy management, and smart grids. Our findings reveal that the developed algorithm detects mechanical faults and security vulnerabilities with an accuracy of 96%, indicating its potential to significantly improve the safety and efficiency of building automation systems. However, the full validation of the algorithm’s performance in various conditions and environments remains a challenge, and future research will explore methodologies to address these issues and further enhance performance. This research is expected to play a vital role in numerous fields, including productivity improvement, data security, and the prevention of human casualties.