A Review of An Interpretable Semi-Supervised System for Detecting Cyberattacks Using Anomaly Detection in Industrial Scenarios [Póster]

Abstract

The Anomaly Detection systems based on Ma chine Learning and Deep Learning techniques showed great performance when detecting cyberattacks in industrial scena rios. 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 Machine Learning and Deep Learning techniques makes it impossible to interpret the decision. This paper proposes an interpretable and semi supervised system to detect cyberattacks in industrial settings. Besides, we validate our proposal using data collected from the Tennessee Eastman Process. To the best of our knowledge, our 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.