Alimi, OAOuahada, KAbu-Mahfouz, Adnan MI2019-10-042019-10-042019-06Alimi, O.A., Ouahada, K. & Abu-Mahfouz, A.M.I. 2019. Real time security assessment of the power system using a hybrid support vector machine and multilayer perceptron neural network algorithms. Sustainability, vol 11(13), pp. 1-182071-1050https://doi.org/10.3390/su11133586https://www.mdpi.com/2071-1050/11/13/3586http://hdl.handle.net/10204/11146This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.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.enCyberattacksHybrid support vector machinesIntruder detection systemsMultilayer perceptron neural networksNetwork algorithmsOptimal power flowPower systemsReal time security assessmentSmart grid securityArticleAlimi, O., Ouahada, K., & Abu-Mahfouz, A. M. (2019). Real time security assessment of the power system using a hybrid support vector machine and multilayer perceptron neural network algorithms. http://hdl.handle.net/10204/11146Alimi, OA, K Ouahada, and Adnan MI Abu-Mahfouz "Real time security assessment of the power system using a hybrid support vector machine and multilayer perceptron neural network algorithms." (2019) http://hdl.handle.net/10204/11146Alimi O, Ouahada K, Abu-Mahfouz AM. Real time security assessment of the power system using a hybrid support vector machine and multilayer perceptron neural network algorithms. 2019; http://hdl.handle.net/10204/11146.TY - Article AU - Alimi, OA AU - Ouahada, K AU - Abu-Mahfouz, Adnan MI AB - 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. DA - 2019-06 DB - ResearchSpace DP - CSIR KW - Cyberattacks KW - Hybrid support vector machines KW - Intruder detection systems KW - Multilayer perceptron neural networks KW - Network algorithms KW - Optimal power flow KW - Power systems KW - Real time security assessment KW - Smart grid security LK - https://researchspace.csir.co.za PY - 2019 SM - 2071-1050 T1 - Real time security assessment of the power system using a hybrid support vector machine and multilayer perceptron neural network algorithms TI - Real time security assessment of the power system using a hybrid support vector machine and multilayer perceptron neural network algorithms UR - http://hdl.handle.net/10204/11146 ER -