9. Applying Machine Learning and Deep Learning Algorithms for the Detection of Physical Anomalies in Critical Water Infrastructures

By Víctor Jimenez, Eurecat, Centre Tecnològic de Catalunya, IT & OT Security Unit, Barcelona, Spain | Juan Caubet, Eurecat, Centre Tecnològic de Catalunya, IT & OT Security Unit, Barcelona, Spain | Mario Reyes, Eurecat, Centre Tecnològic de Catalunya, IT & OT Security Unit, Barcelona, Spain | Nikolaos Bakalos, Institute of Communication and Computer Systems (ICCS), Athens, Greece | Nikolaos Doulamis, Institute of Communication and Computer Systems (ICCS), Athens, Greece | Anastasios Doulamis, Institute of Communication and Computer Systems (ICCS), Athens, Greece | Matthaios Bimpas, Institute of Communication and Computer Systems (ICCS), Athens, Greece

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Published: 15 Sep 2021

© 2021 Víctor Jimenez | Juan Caubet | Mario Reyes | Nikolaos Bakalos | Nikolaos Doulamis | Anastasios Doulamis | Matthaios Bimpas

Abstract

Industrial Control Systems Security implies the safekeeping and protection of such systems, as well as all the software and hardware used by them. Restrict logical and physical access to the ICS devices and networks, securing all individual components of the ICS or avoid unauthorized changes of data are some the main objectives of the ICS security, however, knowing when you are being victim of an attack is more and more important. For this reason, threat detection in industrial infrastructures represents an actual and worthwhile research topic. In this chapter, we present two securitytools developed in the STOP-IT project that apply Machine Learning and Deep Learningalgorithms to detect abnormal behaviours or situations that could become physical threatsfor a Water Infrastructure. A device able to detect the presence of a person in a room or a delimited area by analysing the reflection of WiFi signals in human body and a system able to identify intrusions and abnormal movements or behaviours around the water facility by using improved computer vision techniques.