Article ; Online: Device Classification for Industrial Control Systems Using Predicted Traffic Features
Frontiers in Computer Science, Vol
2022 Volume 4
Abstract: To achieve a secure interconnected Industrial Control System (ICS) architecture, security practitioners depend on accurate identification of network host behavior. However, accurate machine learning based host identification methods depends on the ... ...
| Abstract | To achieve a secure interconnected Industrial Control System (ICS) architecture, security practitioners depend on accurate identification of network host behavior. However, accurate machine learning based host identification methods depends on the availability of significant quantities of network traffic data, which can be difficult to obtain due to system constraints such as network security, data confidentiality, and physical location. In this work, we propose a network traffic feature prediction method based on a generative model, which achieves high host identification accuracy. Furthermore, we develop a joint training algorithm to improve host identification performance compared to separate training of the generative model and the classifier responsible for host identification. |
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| Keywords | synthetic ICS traffic generation ; traffic forecasting ; Seq2Seq modeling ; generative model ; machine learning ; Electronic computers. Computer science ; QA75.5-76.95 |
| Language | English |
| Publishing date | 2022-03-01T00:00:00Z |
| Publisher | Frontiers Media S.A. |
| Document type | Article ; Online |
| Database | BASE - Bielefeld Academic Search Engine (life sciences selection) |
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