RT Journal Article
T1 A robust cyberattack detection approach using optimal features of SCADA power systems in smart grids
A1 Gumaei, Abdu
A1 Hassan, Mohammad Mehedi
A1 Huda, Shamsul
A1 Hassan, Md Rafiul
A1 Camacho, David
A1 Del Ser, Javier
A1 Fortino, Giancarlo
AB Smart grids are a type of complex cyber–physical system (CPS) that integrates the communication capabilities of smart devices into the grid to facilitate remote operation and control of power systems. However, this integration exposes many existing vulnerabilities of conventional supervisory control and data acquisition (SCADA) systems, resulting in severe cyber threats to the smart grid and potential violation of security objectives. Stealing sensitive information, modifying firmware, or injecting function codes through compromised devices are examples of possible attacks on the smart grid. Therefore, early detection of cyberattacks on the grid is crucial to protect it from sabotage. Machine learning (ML) methods are conventional approaches for detecting cyberattacks that use features of smart grid networks. However, developing an effective, highly accurate detection method with reduced computational overload, is still a challenging research problem. In this work, an efficient and effective security control approach is proposed to detect cyberattacks on the smart grid. The proposed approach combines both feature reduction and detection techniques to reduce the extremely large number of features and achieve an improved detection rate. A correlation-based feature selection (CFS) method is used to remove irrelevant features, improving detection efficiency. An instance-based learning (IBL) algorithm classifies normal and cyberattack events using the selected optimal features. This study describes a set of experiments conducted on public datasets from a SCADA power system based on a 10-fold cross-validation technique. Experimental results show that the proposed approach achieves a high detection rate based on a small number of features drawn from SCADA power system measurements.
SN 1568-4946
YR 2020
FD 2020-11
LK https://hdl.handle.net/11556/4313
UL https://hdl.handle.net/11556/4313
LA eng
NO Gumaei , A , Hassan , M M , Huda , S , Hassan , M R , Camacho , D , Del Ser , J & Fortino , G 2020 , ' A robust cyberattack detection approach using optimal features of SCADA power systems in smart grids ' , Applied Soft Computing Journal , vol. 96 , 106658 . https://doi.org/10.1016/j.asoc.2020.106658
NO Publisher Copyright: © 2020 Elsevier B.V.
NO The authors are grateful to King Saud University, Riyadh, Saudi Arabia for funding this work through Researchers Supporting Project number RSP-2020/18 . The work was also partially supported by the Italian MIUR , PRIN 2017 Project ”Fluidware” ( CUP H24I17000070001 )” and the Department of Education of the Basque Government (Consolidated Research Group MATHMODE, IT1294-19 ).
DS TECNALIA Publications
RD 29 jul 2024