Side Channel Identification using Granger Time Series Clustering with Applications to Control Systems

Matthew Lee; Joshua Sylvester; Sunjoli Aggarwal; Aviraj Sinha; Michael Taylor; Nathan Srirama; Eric C. Larson; Mitchell A. Thornton

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Paper

Side Channel Identification using Granger Time Series Clustering with Applications to Control Systems

Topics: Data Mining and Knowledge Discovery; Privacy Enhancing Technologies; Security in IoT and Edge Computing; Vulnerability Analysis and Countermeasures

In Proceedings of the 8th International Conference on Information Systems Security and Privacy ICISSP - Volume 1, 290-298, 2022

Authors: Matthew Lee ; Joshua Sylvester ; Sunjoli Aggarwal ; Aviraj Sinha ; Michael Taylor ; Nathan Srirama ; Eric C. Larson and Mitchell A. Thornton

Affiliation: Darwin Deason Institute for Cyber Security, Southern Methodist University, Dallas, Texas, U.S.A.

Keyword(s): Side Channel, Granger Causality, Clustering, Industrial Control Systems.

Abstract: Side channels are data sources that adversaries can exploit to carry out cyber security attacks. Alternatively, side channels can be used as data sources for techniques to predict the presence of an attack. Typically, the identification of side channels requires domain-specific expertise and it is likely that many side channels are present within systems that are not readily identified, even by a subject matter expert. We are motivated to develop methods that automatically recognize the presence of side channels without requiring the need to use detailed or domain-specific knowledge. Understanding cause and effect relationships is hypothesized to be a key aspect of determining appropriate side channels; however, determining such relationships is generally a problem whose solution is very challenging. We describe a time-series clustering approach for identifying side channels using the statistical model of Granger causality. Since our method is based upon the Granger causality paradig m in contrast to techniques that rely upon the identification of correlation relationships, we can identify side channels without requiring detailed subject matter expertise. A Granger-based data clustering technique is described in detail and experimental results of our prototype algorithms are provided to demonstrate the efficacy of the approach using an industrial control system model comprised of commercial components. (More)

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Paper citation in several formats:

Lee, M.; Sylvester, J.; Aggarwal, S.; Sinha, A.; Taylor, M.; Srirama, N.; Larson, E. and Thornton, M. (2022). Side Channel Identification using Granger Time Series Clustering with Applications to Control Systems. In Proceedings of the 8th International Conference on Information Systems Security and Privacy - ICISSP; ISBN 978-989-758-553-1; ISSN 2184-4356, SciTePress, pages 290-298. DOI: 10.5220/0010781600003120

@conference{icissp22,
author={Matthew Lee. and Joshua Sylvester. and Sunjoli Aggarwal. and Aviraj Sinha. and Michael Taylor. and Nathan Srirama. and Eric C. Larson. and Mitchell A. Thornton.},
title={Side Channel Identification using Granger Time Series Clustering with Applications to Control Systems},
booktitle={Proceedings of the 8th International Conference on Information Systems Security and Privacy - ICISSP},
year={2022},
pages={290-298},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010781600003120},
isbn={978-989-758-553-1},
issn={2184-4356},
}

TY - CONF

JO - Proceedings of the 8th International Conference on Information Systems Security and Privacy - ICISSP
TI - Side Channel Identification using Granger Time Series Clustering with Applications to Control Systems
SN - 978-989-758-553-1
IS - 2184-4356
AU - Lee, M.
AU - Sylvester, J.
AU - Aggarwal, S.
AU - Sinha, A.
AU - Taylor, M.
AU - Srirama, N.
AU - Larson, E.
AU - Thornton, M.
PY - 2022
SP - 290
EP - 298
DO - 10.5220/0010781600003120
PB - SciTePress