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Intelligent intrusion detection system in smart grid using computational intelligence and machine learning
Author(s) -
Khan Suleman,
Kifayat Kashif,
Kashif Bashir Ali,
Gurtov Andrei,
Hassan Mehdi
Publication year - 2021
Publication title -
transactions on emerging telecommunications technologies
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.366
H-Index - 47
ISSN - 2161-3915
DOI - 10.1002/ett.4062
Subject(s) - computer science , smart grid , intrusion detection system , constant false alarm rate , random forest , grid , artificial intelligence , data mining , artificial neural network , machine learning , alarm , real time computing , computer security , engineering , geometry , mathematics , aerospace engineering , electrical engineering
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.