Detecting Cyber Attacks in Industrial Control Systems Using Convolutional Neural Networks

By Moshe Kravchik, Asaf Shabtai. CPS-SPC@CCS, 2018

329 citations recorded by the source corpus.

This paper presents a study on detecting cyber attacks on industrial control systems (ICS) using convolutional neural networks. The study was performed on a Secure Water Treatment testbed (SWaT) dataset, which represents a scaled-down version of a real-world industrial water treatment plant. We suggest a method for anomaly detection based on measuring the statistical deviation of the predicted value from the observed value. We applied the proposed method by using a variety of deep neural network architectures including different variants of convolutional and recurrent networks. The test dataset included 36 different cyber attacks. The proposed method successfully detected 31 attacks with three false positives thus improving on previous research based on this dataset. The results of the study show that 1D convolutional networks can be successfully used for anomaly detection in industrial control systems and outperform recurrent networks in this setting. The findings also suggest that 1D convolutional networks are effective at time series prediction tasks which are traditionally considered to be best solved using recurrent neural networks. This observation is a promising one, as 1D convolutional neural networks are simpler, smaller, and faster than the recurrent neural networks.

Subfields: Artificial Intelligence, Computer Networks and Communications, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/e609d97b5735f94061a95a78416473363504910c

How researchers see this paper

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In Artificial Intelligence

1. Jindong Wang
2. Yiqiang Chen
3. Yu-xin Zhang
4. Zhiwen Pan

In Computer Networks and Communications

1. Jindong Wang
2. Yu-xin Zhang
3. Yiqiang Chen
4. Zhiwen Pan

In Control and Systems Engineering

1. Jindong Wang
2. Yu-xin Zhang
3. Yiqiang Chen
4. Zhiwen Pan

Citations in the corpus

• Unsupervised Deep Anomaly Detection for Multi-Sensor Time-Series Signals (2021, background): “Kravchik and Shabtai [44] apply different variants of convolutional and recurrent networks to perform forecasting model.”