Real Time Security Assessment of the Power System Using a Hybrid Support Vector Machine and Multilayer Perceptron Neural Network Algorithms

Abstract

In today’s grid, the technological based cyber-physical systems have continued to be plagued with cyberattacks and intrusions. Any intrusive action on the power system’s Optimal Power Flow (OPF) modules can cause a series of operational instabilities, failures, and financial losses. Real time intrusion detection has become a major challenge for the power community and energy stakeholders. Current conventional methods have continued to exhibit shortfalls in tackling these security issues. In order to address this security issue, this paper proposes a hybrid Support Vector Machine and Multilayer Perceptron Neural Network (SVMNN) algorithm that involves the combination of Support Vector Machine (SVM) and multilayer perceptron neural network (MPLNN) algorithms for predicting and detecting cyber intrusion attacks into power system networks. In this paper, a modified version of the IEEE Garver 6-bus test system and a 24-bus system were used as case studies. The IEEE Garver 6-bus test system was used to describe the attack scenarios, whereas load flow analysis was conducted on real time data of a modified Nigerian 24-bus system to generate the bus voltage dataset that considered several cyberattack events for the hybrid algorithm. Sising various performance metricion and load/generator injections, en included in the manuscriptmulation results showed the relevant influences of cyberattacks on power systems in terms of voltage, power, and current flows. To demonstrate the performance of the proposed hybrid SVMNN algorithm, the results are compared with other models in related studies. The results demonstrated that the hybrid algorithm achieved a detection accuracy of 99.6%, which is better than recently proposed schemes.

A Review of Machine Learning Approaches to Power System Security and Stability

“…These threats and attacks are projected to escalate in geometric rates in the nearest future as intruders/attackers find the energy infrastructures (arguably the most important of all CI) as a lucrative avenue to gain attention [31]. The Industrial Control Systems Cyber Emergency Response Team (ICS-CERT) announced that, out of the 245 recorded cyber incidents on CI in 2014, 79 were targeted at the energy sector [2]. Based on the motives and the cause of attacks, SCADA threats and attacks can be categorized as [32]: 1.…”

“…Over the past few decades, power system operations are constantly being modernized so as to accommodate the integration of renewable energy and storage systems (RES), liberalized market, numerous measuring and communication technologies devices to name a few [1]. While the modernization contributed immensely to safer, reliable and cleaner energy distribution to users, the transition also brings along new challenges to the network's security and stability [2]. The overreliance of modern power system's applications such as state estimation, Supervisory Control and Data Acquisition (SCADA) systems, Phasor Measurement Unit (PMU) on open communication technologies including the internet have exposed the networks to various vulnerabilities and threats [3].…”

Intrusion Detection System Based on Integrated System Calls Graph and Neural Networks

“…Following the same idea, another paper combines support vector machine and another technique in a network intrusion detection system [49], [50]. In [51], a host detection system based on system calls combines clustering and Bayes techniques.…”

Integrating Qualitative Comparative Analysis and Support Vector Machine Methods to Reduce Passengers’ Resistance to Biometric E-Gates for Sustainable Airport Operations

“…Zu et al [8] Optimal Precision Alimi et al [9] Linear, Poly, RBF Precision, Recall, F-Score Xue et al [10] RBF Accuracy Olivares-Mercado et al [12] RBF Precision, Recall, Accuracy, F-Score Joshi et al [59] RBF Accuracy Ahmad et al [16] RBF Accuracy Aruna et al [60] RBF Accuracy Abdelaal et al [15] RBF AUC You and Rumbe [20] Poly, RBF, Sigmoid Accuracy Huang et al [17] RBF Accuracy…”

“…The SVM methodology, which belongs to intellectual machine-learning algorithms, has been actively used within the field of sustainability research [8][9][10][11][12]. Among the machine-learning algorithms, such as linear discriminate analysis, decision trees, logistic regression, naïve Bayes, artificial neural networks and k-nearest neighbor, SVM is a tried and tested algorithm that has gained much trust amongst academics [13,14].…”