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- Title
Intelligent intrusion detection system in smart grid using computational intelligence and machine learning.
- Authors
Khan, Suleman; Kifayat, Kashif; Kashif Bashir, Ali; Gurtov, Andrei; Hassan, Mehdi
- Abstract
Smart grid systems enhanced the capability of traditional power networks while being vulnerable to different types of cyber‐attacks. These vulnerabilities could cause attackers to crash into the network breaching the integrity and confidentiality of the smart grid systems. Therefore, an intrusion detection system (IDS) becomes an important way to provide a secure and reliable services in a smart grid environment. This article proposes a feature‐based IDS for smart grid systems. The proposed system performance is evaluated in terms of accuracy, intrusion detection rate (DR), and false alarm rate (FAR). The obtained results show that the random forest and neural network classifiers have outperformed other classifiers. We have achieved a 0.5% FAR on KDD99 dataset and a 0.08% FAR on the NSLKDD dataset. The DR and the testing accuracy on average are 99% for both datasets.
- Subjects
COMPUTATIONAL intelligence; GRIDS (Cartography); SMART power grids; MACHINE learning; RANDOM forest algorithms; FALSE alarms
- Publication
Transactions on Emerging Telecommunications Technologies, 2021, Vol 32, Issue 6, p1
- ISSN
2161-3915
- Publication type
Article
- DOI
10.1002/ett.4062