Devil in the Detail: Attack Scenarios in Industrial Applications

Simon Duque Antón; Alexander Hafner; Hans Dieter Schotten

In: Proceedings of the 40th IEEE Symposium on Security and Privacy Workshops (SPW). IEEE Symposium on Security and Privacy Workshops (SPW-2019), May 23, San Francisco, CA, USA, IEEE, 2019.


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.


Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence