Intelligent intrusion detection system in smart grid using computational intelligence and machine learning,Transactions on Emerging Telecommunications Technologies

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Intelligent intrusion detection system in smart grid using computational intelligence and machine learning
Transactions on Emerging Telecommunications Technologies ( IF 2.5 ) Pub Date : 2020-08-03 , DOI: 10.1002/ett.4062
Suleman Khan ₁ , Kashif Kifayat ₁ , Ali Kashif Bashir ₂ , Andrei Gurtov ₃ , Mehdi Hassan ₁

Affiliation

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.

中文翻译：

基于计算智能和机器学习的智能电网智能入侵检测系统

智能电网系统增强了传统电力网络的能力，同时容易受到不同类型的网络攻击。这些漏洞可能会导致攻击者闯入网络，从而破坏智能电网系统的完整性和机密性。因此，入侵检测系统（IDS）成为在智能电网环境中提供安全可靠服务的重要途径。本文提出了一种用于智能电网系统的基于特征的 IDS。建议的系统性能在准确性、入侵检测率 (DR) 和误报率 (FAR) 方面进行评估。得到的结果表明，随机森林和神经网络分类器的性能优于其他分类器。我们在 KDD99 数据集上实现了 0.5% 的 FAR，在 NSLKDD 数据集上实现了 0.08% 的 FAR。

更新日期：2020-08-03

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