Intrusion Detection in SCADA Systems Using One-Class Classification
Résumé
Supervisory Control and Data Acquisition (SCADA) systems allow remote monitoring and control of critical infrastructures such as electrical power grids, gas pipelines, nuclear power plants, etc. Cyberattacks threatening these infrastructures may cause serious economic losses and may impact the health and safety of the employees and the citizens living in the area. The diversity of cyberattacks and the complexity of the studied systems make modeling cyberattacks very difficult or even impossible. This paper outlines the importance of one-class classification in detecting intrusions in SCADA systems. Two approaches are investigated, the SupportVector Data Description and the Kernel Principal Component Analysis. A case study on a gas pipeline testbed is provided with real data containing many types of cyberattacks.
Mots clés
one-class
cybersecurity
pattern classification
principal component analysis
SCADA systems
security of data
supervisory control and data acquisition systems
remote monitoring
critical infrastructures
electrical power grids
gas pipelines
nuclear power plants
cyberattacks
economic losses
employee health
employee safety
cyberattack modeling
support vector data description
kernel principal component analysis
gas pipeline testbed
Kernel
Computer crime
Pipelines
Training
Support vector machines
Intrusion detection
One-class classification
kernel methods
novelty detection
machine learning
Origine | Fichiers produits par l'(les) auteur(s) |
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