Journal
IEEE INTERNET OF THINGS JOURNAL
Volume 8, Issue 17, Pages 13712-13722Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JIOT.2021.3067667
Keywords
Integrated circuits; Support vector machines; Industrial Internet of Things; Feature extraction; Pipelines; Neural networks; Mathematical model; Cyber threat attribution; cyber threat detection; cyber-physical systems (CPS); cyber-attacks; deep representation learning; industrial control system (ICS); Industrial Internet of Things (IIoT)
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Funding
- National Science Foundation CREST [HRD-1736209]
- Cloud Technology Endowed Professorship
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The article introduces a two-level ensemble attack detection and attribution framework designed for cyber-physical systems, which outperforms other competing methods.
Securing Internet-of-Things (IoT)-enabled cyber-physical systems (CPS) can be challenging, as security solutions developed for general information/operational technology (IT/OT) systems may not be as effective in a CPS setting. Thus, this article presents a two-level ensemble attack detection and attribution framework designed for CPS, and more specifically in an industrial control system (ICS). At the first level, a decision tree combined with a novel ensemble deep representation-learning model is developed for detecting attacks imbalanced ICS environments. At the second level, an ensemble deep neural network is designed to facilitate attack attribution. The proposed model is evaluated using real-world data sets in gas pipeline and water treatment system. Findings demonstrate that the proposed model outperforms other competing approaches with similar computational complexity.
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