Open AccessFlow‐based intrusion detection algorithm for supervisory control and data acquisition systems: A real‐time approach
Author(s) -
Teixeira Marcio Andrey,
Zolanvari Maede,
Khan Khaled M.,
Jain Raj,
Meskin Nader
Publication year - 2021
Publication title -
iet cyber‐physical systems: theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.308
H-Index - 7
ISSN - 2398-3396
DOI - 10.1049/cps2.12016
Subject(s) - scada , intrusion detection system , computer science , data mining , supervisory control , real time computing , industrial control system , algorithm , control (management) , artificial intelligence , engineering , electrical engineering
Intrusion detection in supervisory control and data acquisition (SCADA) systems is integral because of the critical roles of these systems in industries. However, available approaches in the literature lack representative flow‐based datasets and reliable real‐time adaption and evaluation. A publicly available labelled dataset to support flow‐based intrusion detection research specific to SCADA systems is presented. Cyberattacks were carried out against our SCADA system test bed to generate this flow‐based dataset. Moreover, a flow‐based intrusion detection system (IDS) is developed for SCADA systems using a deep learning algorithm. We used the dataset to develop this IDS model for real‐time operations of SCADA systems to detect attacks momentarily after they happen. The results show empirical proof of the model’s adequacy when deployed online to detect cyberattacks in real time.