The DOI matches its Crossref record; 44 of the 44 references it cites were found in a registry or an index.
Detecting Cyber Attacks in Industrial Control Systems Using Convolutional Neural Networks
Abstract
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.
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Cites
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Cited by
Deep Learning for Anomaly Detection: A Survey
Chalapathy, R., & Chawla, S. (2019) · arXiv · doi:10.48550/arxiv.1901.03407 · Cited by 1,240
Deep Learning Based Attack Detection for Cyber-Physical System Cybersecurity: A Survey
Zhang, J., Pan, L., Han, Q.-L., Chen, C., Wen, S., & Xiang, Y. (2022) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2021.1004261 · Cited by 432
Unsupervised Deep Anomaly Detection for Multi-Sensor Time-Series Signals
Zhang, Y., Chen, Y., Wang, J., & Pan, Z. (2021) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2021.3102110 · Cited by 312
Adversarial Machine Learning Attacks and Defense Methods in the Cyber Security Domain
Rosenberg, I., Shabtai, A., Elovici, Y., & Rokach, L. (2022) · ACM Computing Surveys · doi:10.1145/3453158 · Cited by 295
Self-citationDeep Learning-based Anomaly Detection in Cyber-physical Systems: Progress and Opportunities
Luo, Y., Xiao, Y., Cheng, L., Peng, G., & Yao, D. (. (2022) · ACM Computing Surveys · doi:10.1145/3453155 · Cited by 268
Metadata
- Authors
- Kravchik, M., & Shabtai, A.
- Venue
- Proceedings of the 2018 Workshop on Cyber-Physical Systems Security and PrivaCy
- Publisher
- ACM
- Published
- 2018-01-15
- Pages
- 72-83
- OpenAlex
- W2896370767
- Cited by
- 355
- References
- 44
- FWCI
- 28.40
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Kravchik, M., & Shabtai, A. (2018). Detecting Cyber Attacks in Industrial Control Systems Using Convolutional Neural Networks. Proceedings of the 2018 Workshop on Cyber-Physical Systems Security and PrivaCy. https://doi.org/10.1145/3264888.3264896
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