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AICrit: A Design-Enhanced Anomaly Detector and Its Performance Assessment in a Water Treatment Plant

Authors :
Gauthama Raman
Aditya Mathur
Source :
Applied Sciences, Vol 13, Iss 24, p 13124 (2023)
Publication Year :
2023
Publisher :
MDPI AG, 2023.

Abstract

Critical Infrastructure Security Showdown 2021—Online (CISS2021-OL) represented the fifth run of iTrust’s international technology assessment exercise. During this event, researchers and experts from the industry evaluated the performance of technologies designed to detect and mitigate real-time cyber-physical attacks launched against the operational iTrust testbeds and digital twins. Here, we summarize the performance of an anomaly detection mechanism, named AICrit, that was used during the exercise. AICrit utilizes the plant’s design to determine the models to be created using machine learning, and hence is referred to as a “design-enhanced” anomaly detector. The results of the validation in this large-scale exercise reveal that AICrit successfully detected 95.83% of the 27 launched attacks. Our analysis offers valuable insights into AICrit’s efficiency in detecting process anomalies in a water treatment plant under a continuous barrage of cyber-physical attacks.

Details

Language :
English
ISSN :
20763417
Volume :
13
Issue :
24
Database :
Directory of Open Access Journals
Journal :
Applied Sciences
Publication Type :
Academic Journal
Accession number :
edsdoj.18d67512a2cd41cf8ce034f238b3adca
Document Type :
article
Full Text :
https://doi.org/10.3390/app132413124