Cited by 122Smart Grid Security and ResilienceNetwork Security and Intrusion DetectionArtificial Immune Systems Applications

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${l_p}$-norms in One-Class Classification for Intrusion Detection in SCADA Systems

Nader, Patric, Honeine, Paul, Beauseroy, Pierre·2014·IEEE Transactions on Industrial Informatics

DOI 10.1109/tii.2014.2330796

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Abstract

The massive use of information and communication technologies in supervisory control and data acquisition (SCADA) systems opens new ways for carrying out cyberattacks against critical infrastructures relying on SCADA networks. The various vulnerabilities in these systems and the heterogeneity of cyberattacks make the task extremely difficult for traditional intrusion detection systems (IDS). Modeling cyberattacks has become nearly impossible and their potential consequences may be very severe. The primary objective of this work is to detect malicious intrusions once they have already bypassed traditional IDS and firewalls. This paper investigates the use of machine learning for intrusion detection in SCADA systems using one-class classification algorithms. Two approaches of one-class classification are investigated: 1) the support vector data description (SVDD); and 2) the kernel principle component analysis. The impact of the considered metric is examined in detail with the study of lp-norms in radial basis function (RBF) kernels. A heuristic is proposed to find an optimal choice of the bandwidth parameter in these kernels. Tests are conducted on real data with several types of cyberattacks.

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Authors: Nader, P., Honeine, P., & Beauseroy, P.
Affiliations: Centre National de la Recherche Scientifique (FR)
ORCID: Beauseroy, Pierre
Venue: IEEE Transactions on Industrial Informatics
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2014-11
Volume: 10
Issue: 4
Pages: 2308-2317
ISSN: 1551-3203 (ISSN-L)
1941-0050
DOI: 10.1109/tii.2014.2330796
OpenAlex: W2087432251
Cited by: 122
References: 59
Citations by year: 2026: 2 · 2025: 7 · 2024: 3 · 2023: 6 · 2022: 17 · 2021: 17 · 2020: 22 · 2019: 11 · 2018: 13 · 2017: 10 · 2016: 7 · 2015: 5 · 2014: 2
FWCI: 8.92
Type: journal article
Language: en
Open access: closed
Licence: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
Locations: IEEE Transactions on Industrial Informatics (publishedVersion)
HAL (submittedVersion)
Funders: Mississippi State University
Université Saint-Esprit de Kaslik
Agence Nationale de la Recherche
Topics: Physical Sciences › Engineering › Control and Systems Engineering › Smart Grid Security and Resilience
Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
Physical Sciences › Computer Science › Artificial Intelligence › Artificial Immune Systems Applications
Keywords: intrusion detection, SCADA systems, one-class classification, Lp norms, support vector data description, kernel principal component analysis, Gaussian kernel, industrial cybersecurity

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${l_p}$-norms in One-Class Classification for Intrusion Detection in SCADA Systems

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