Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/145800
Title: A standardized ICS network data processing flow with generative model in anomaly detection
Authors: Yang, Tao
Hu, Yibo
Li, Yang
Hu, Wei
Pan, Quan
Keywords: Engineering::Computer science and engineering
Issue Date: 2020
Source: Yang, T., Hu, Y., Li, Y., Hu, W., & Pan, Q. (2020). A standardized ICS network data processing flow with generative model in anomaly detection. IEEE Access, 8, 4255-4264. doi:10.1109/access.2019.2963144
Journal: IEEE Access
Abstract: Industrial control systems (ICS) now usually connect to Wireless Sensor Networks and the Internet, exposing them to security threats resulting from cyber-attacks. However, detecting such attacks is non-trivial task. The high-dimensional network data pose significant challenges on security anomaly detection. In this work, we propose a network flow data processing method, which can make the complex network data more standardized and unified to assist security anomaly detection. Then, data generation method is applied to collect enough training data. We also propose a evaluation method for generated data. Finally, the bidirectional recurrent neural networks with attention mechanism is proposed to extract the latent feature, and give an explainable results in identifying the dominant attributes. Empirical results show our method outperforms the state-of-the-art models.
URI: https://hdl.handle.net/10356/145800
ISSN: 2169-3536
DOI: 10.1109/ACCESS.2019.2963144
Rights: © 2020 IEEE. This journal is 100% open access, which means that all content is freely available without charge to users or their institutions. All articles accepted after 12 June 2019 are published under a CC BY 4.0 license, and the author retains copyright. Users are allowed to read, download, copy, distribute, print, search, or link to the full texts of the articles, or use them for any other lawful purpose, as long as proper attribution is given.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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