Devil in the Detail: Attack Scenarios in Industrial Applications
This addresses security vulnerabilities in industrial networks, which are critical for protecting cyber-physical systems from attacks with physical consequences, but the approach is incremental as it applies existing methods to a specific domain.
The paper tackles the problem of detecting attacks in industrial networks by categorizing attack vectors and simulating them on a real-world process, then employing two machine learning-based anomaly detection methods, with Matrix Profiles achieving better results than Long Short-Term Memory networks.
In the past years, industrial networks have become increasingly interconnected and opened to private or public networks. This leads to an increase in efficiency and manageability, but also increases the attack surface. Industrial networks often consist of legacy systems that have not been designed with security in mind. In the last decade, an increase in attacks on cyber-physical systems was observed, with drastic consequences on the physical work. In this work, attack vectors on industrial networks are categorised. A real-world process is simulated, attacks are then introduced. Finally, two machine learning-based methods for time series anomaly detection are employed to detect the attacks. Matrix Profiles are employed more successfully than a predictor Long Short-Term Memory network, a class of neural networks.