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- Title
An interpretable semi‐supervised system for detecting cyberattacks using anomaly detection in industrial scenarios.
- Authors
Perales Gómez, Ángel Luis; Fernández Maimó, Lorenzo; Huertas Celdrán, Alberto; García Clemente, Félix J.
- Abstract
When detecting cyberattacks in Industrial settings, it is not sufficient to determine whether the system is suffering a cyberattack. It is also fundamental to explain why the system is under a cyberattack and which are the assets affected. In this context, the Anomaly Detection based on Machine Learning (ML) and Deep Learning (DL) techniques showed great performance when detecting cyberattacks in industrial scenarios. However, two main limitations hinder using them in a real environment. Firstly, most solutions are trained using a supervised approach, which is impractical in the real industrial world. Secondly, the use of black‐box ML and DL techniques makes it impossible to interpret the decision made by the model. This article proposes an interpretable and semi‐supervised system to detect cyberattacks in Industrial settings. Besides, our proposal was validated using data collected from the Tennessee Eastman Process. To the best of our knowledge, this system is the only one that offers interpretability together with a semi‐supervised approach in an industrial setting. Our system discriminates between causes and effects of anomalies and also achieved the best performance for 11 types of anomalies out of 20 with an overall recall of 0.9577, a precision of 0.9977, and a F1‐score of 0.9711.
- Subjects
ANOMALY detection (Computer security); INTRUSION detection systems (Computer security); CYBERTERRORISM; DEEP learning; MACHINE learning; SUPERVISED learning
- Publication
IET Information Security (Wiley-Blackwell), 2023, Vol 17, Issue 4, p553
- ISSN
1751-8709
- Publication type
Article
- DOI
10.1049/ise2.12115