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• W4200510573 abstract "The security of Industrial Control Systems is of high importance as they play a critical role in uninterrupted services provided by Critical Infrastructure operators. Due to a large number of devices and their geographical distribution, Industrial Control Systems need efficient automatic cyber-attack detection and attribution methods, which suggests us AI-based approaches. This paper proposes a model called SteelEye based on Semi-Deep Learning for accurate detection and attribution of cyber-attacks at the application layer in industrial control systems. The proposed model depends on Bag of Features for accurate detection of cyber-attacks and utilizes Categorical Boosting as the base predictor for attack attribution. Empirical results demonstrate that SteelEye remarkably outperforms state-of-the-art cyber-attack detection and attribution methods in terms of accuracy, precision, recall, and Fl-score." @default.
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• W4200510573 date "2021-12-13" @default.
• W4200510573 modified "2023-10-17" @default.
• W4200510573 title "SteelEye: An Application-Layer Attack Detection and Attribution Model in Industrial Control Systems using Semi-Deep Learning" @default.
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• W4200510573 doi "https://doi.org/10.1109/pst52912.2021.9647777" @default.
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