@proceedings {7258,
	title = {Detecting Cyberthreats in Smart Grids Using Small-Scale Machine Learning},
	journal = {ELECTRIMACS 2022},
	year = {2022},
	address = {Nancy, France},
	abstract = {Due to advanced monitoring technologies including the plug-in of the cyber and physical layers on the Internet, cyber-physical systems are becoming more vulnerable than ever to cyberthreats leading to possible damage of the system. Consequently, many researchers have devoted to studying detection and identification of such threats in order to mitigate their drawbacks. Among used tools, Machine Learning (ML) has become dominant in the field due to many usability characteristics including the blackbox models availability. In this context, this paper is dedicated to the detection of cyberattacks in Smart Grid (SG) networks which uses industrial control systems (ICS), through the integration of ML models assembled on a small scale. More precisely, it therefore aims to study an electric traction substation system used for the railway industry. The main novelty of our contribution lies in the study of the behaviour of more realistic data than the traditional studies previously shown in the state of the art literature by investigating even more realistic types of attacks. It also emulates data analysis and a larger feature space under most commonly used connectivity protocols in today{\textquoteright}s industry such as S7Comm and Modbus.},
	url = {https://www.researchgate.net/publication/360756643_Detecting_Cyberthreats_in_Smart_Grids_Using_Small-Scale_Machine_Learning},
	author = {Berghout, Tarek and Benbouzid, Mohamed}
}
