TY - GEN
T1 - Detection of Cyberattacks in SCADA Water Distribution Systems Using Machine Learning
T2 - International Conference on Computer Science, Electronics and Industrial Engineering, CSEI 2023
AU - Galarza Yallico, Amanda Liliana
AU - Santos López, Félix Melchor
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
PY - 2024
Y1 - 2024
N2 - Various industries use supervisory control and data acquisition (SCADA) systems to monitor and control different processes, one of which is water distribution systems. In recent years, intentional cyberattacks targeting these systems have increased. It is essential to protect them, and intelligent technologies, such as machine learning, can guarantee their productivity and safety. The objective of this study is to describe the different models, techniques, metrics, datasets, and machine learning algorithms applied in the detection of cyberattacks through a systematic review of the literature using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) methodology. The databases consulted were Web of Science, Scopus, Springer, ScienceDirect, and IEEE, from which 656 articles were retrieved. An exhaustive bibliometric analysis was carried out, and the 40 most relevant articles were selected. The results show that themost used models are artificial neural networks (five mentions) and K-nearest neighbors (five mentions). In addition, one article had an accuracy metric of 99.79%.
AB - Various industries use supervisory control and data acquisition (SCADA) systems to monitor and control different processes, one of which is water distribution systems. In recent years, intentional cyberattacks targeting these systems have increased. It is essential to protect them, and intelligent technologies, such as machine learning, can guarantee their productivity and safety. The objective of this study is to describe the different models, techniques, metrics, datasets, and machine learning algorithms applied in the detection of cyberattacks through a systematic review of the literature using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) methodology. The databases consulted were Web of Science, Scopus, Springer, ScienceDirect, and IEEE, from which 656 articles were retrieved. An exhaustive bibliometric analysis was carried out, and the 40 most relevant articles were selected. The results show that themost used models are artificial neural networks (five mentions) and K-nearest neighbors (five mentions). In addition, one article had an accuracy metric of 99.79%.
KW - Machine learning
KW - SCADA system
KW - detection of cyberattacks
KW - water distribution systems
UR - https://www.scopus.com/pages/publications/85214011853
U2 - 10.1007/978-3-031-69228-4_29
DO - 10.1007/978-3-031-69228-4_29
M3 - Conference contribution
AN - SCOPUS:85214011853
SN - 9783031692277
T3 - Lecture Notes in Networks and Systems
SP - 428
EP - 444
BT - Proceedings of the International Conference on Computer Science, Electronics and Industrial Engineering (CSEI 2023) - Advances in Computer Sciences - Exploring Innovations at the Intersection of Computing Technologies
A2 - Garcia, Marcelo V.
A2 - Gordón-Gallegos, Carlos
A2 - Salazar-Ramírez, Asier
A2 - Nuñez, Carlos
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 6 November 2023 through 10 November 2023
ER -