Devil in the Detail: Attack Scenarios in Industrial Applications

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 w...

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Bibliographic Details
Published in2019 IEEE Security and Privacy Workshops (SPW) pp. 169 - 174
Main Authors Duque Anton, Simon D., Hafner, Alexander, Schotten, Hans Dieter
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.05.2019
Subjects
Containers
Cyber Security
Industrial Control Systems
Intrusion detection
Machine Learning
Malware
Neural Networks
Sensors
Time Series
Time series analysis
Valves
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Summary: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.
DOI:10.1109/SPW.2019.00040