SteelEye: An Application-Layer Attack Detection and Attribution Model in Industrial Control Systems using Semi-Deep Learning

Sanaz Nakhodchi, Behrouz Zolfaghari, Abbas Yazdinejad, Ali Dehghantanha

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

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.

本文言語English
ホスト出版物のタイトル2021 18th International Conference on Privacy, Security and Trust, PST 2021
出版社Institute of Electrical and Electronics Engineers Inc.
ISBN(電子版)9781665401845
DOI
出版ステータスPublished - 2021
外部発表はい
イベント18th International Conference on Privacy, Security and Trust, PST 2021 - Auckland, New Zealand
継続期間: 2021 12月 132021 12月 15

出版物シリーズ

名前2021 18th International Conference on Privacy, Security and Trust, PST 2021

Conference

Conference18th International Conference on Privacy, Security and Trust, PST 2021
国/地域New Zealand
CityAuckland
Period21/12/1321/12/15

ASJC Scopus subject areas

  • 情報システム
  • 情報システムおよび情報管理
  • 安全性、リスク、信頼性、品質管理
  • 器械工学

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