Research on Attack Path Discovery of Power Information Network Based on Bayesian Inference

[1] Papia, R., Raj, A. S., & Prasad, M. D. (2018). Intelligence scheme for fault location in a combined overhead transmission line & underground cable. International Journal of Emerging Electric Power Systems, 19. Search in Google Scholar

[2] Askarzadeh, & Alireza. (2017). Solving electrical power system problems by harmony search: a review. Artificial Intelligence Review, 47(2), 1-35. Search in Google Scholar

[3] Tang, Y., Cui, H., Li, F., & Wang, Q. (2019). Review on artificial intelligence in power system transient stability analysis. Zhongguo Dianji Gongcheng Xuebao/Proceedings of the Chinese Society of Electrical Engineering, 39(1), 2-13. Search in Google Scholar

[4] Jiang, D. Y., Zhang, H., Kumar, H., Naveed, Q. N., Takhi, C., & Jagota, V., et al. (2022). Automatic control model of power information system access based on artificial intelligence technology. Mathematical Problems in Engineering, 2022. Search in Google Scholar

[5] Stock, S., Babazadeh, D., & Becker, C. (2021). Applications of artificial intelligence in distribution power system operation. IEEE Access. Search in Google Scholar

[6] Sharma, S., Sabitha, B., Prabhakaran, A., Chavan, M., & Srivastava, R. (2022). A hybrid swarm intelligence approach for resolving reactive power dispatch issues in power system: optimal placement and sizing of upfc. Advances in engineering software. Search in Google Scholar

[7] Vincent, E., Korki, M., Seyedmahmoudian, M., Stojcevski, A., & Mekhilef, S. (2023). Detection of false data injection attacks in cyber–physical systems using graph convolutional network. Electric Power Systems Research, 217, 109118-. Search in Google Scholar

[8] Sagar, D., & Saidireddy, M. (2023). Security measurement in lte/lte-a network based on zs-lr feature selection technique and um-tgan attack detection technique. Expert Systems with Applications. Search in Google Scholar

[9] Milano, F., & Gomez-Exposito, A. (2020). Detection of cyber-attacks of power systems through benford’s law. IEEE Transactions on Smart Grid, PP(99), 1-1. Search in Google Scholar

[10] Alimi, O. A., Ouahada, K., & Abu-Mahfouz, A. M. (2020). A review of machine learning approaches to power system security and stability. IEEE Access, PP(99), 1-1. Search in Google Scholar

[11] Kontouras, E., Tzes, A., & Dritsas, L. (2019). Hybrid detection of intermittent cyber-attacks in networked power systems. Energies, 12(24), 4625. Search in Google Scholar

[12] Huang, R., & Li, Y. (2023). Adversarial attack mitigation strategy for machine learning-based network attack detection model in power system. IEEE Transactions on Smart Grid, 14, 2367-2376. Search in Google Scholar

[13] Ding, Y., Ma, K., Pu, T., Wang, X., & Zhang, D. (2021). A deep learning-based classification scheme for false data injection attack detection in power system. Electronics, 10(12), 1459. Search in Google Scholar

[14] Wu, M., Roy, R., Torre, P. S., & Hidalgo-Gonzalez, P. (2022). Effectiveness of learning algorithms with attack and defense mechanisms for power systems. Electric Power Systems Research. Search in Google Scholar

[15] Fei, J., Yao, Q., Chen, M., Wang, X., & Fan, J. (2020). The abnormal detection for network traffic of power iot based on device portrait. Scientific Programming, 2020(9), 1-9. Search in Google Scholar

[16] Li, Y. W. Y. (2020). Developing graphical detection techniques for maintaining state estimation integrity against false data injection attack in integrated electric cyber-physical system. Journal of systems architecture, 105(1). Search in Google Scholar

[17] Wang, Y., Xing, A., Qu, Z., Han, X., Dong, H., & Georgievitch, P. M. (2022). False data injection attack detection based on interval affine state estimation. Electric Power Systems Research. Search in Google Scholar

[18] Aldwairi, M., & Alansari, D. (2019). N-grams exclusion and inclusion filter for intrusion detection in internet of energy big data systems. Transactions on Emerging Telecommunications Technologies(16). Search in Google Scholar

[19] Kwon, S., Yoo, H., & Shon, T. (2020). Ieee 1815.1-based power system security with bidirectional rnn-based network anomalous attack detection for cyber-physical system. IEEE Access, PP(99), 1-1. Search in Google Scholar

[20] Shefaei, A., Mohammadpourfard, M., Mohammadi-Ivatloo, B., & Weng, Y. (2021). Revealing a new vulnerability of distributed state estimation: a data integrity attack and an unsupervised detection algorithm. IEEE Transactions on Control of Network Systems, PP(99), 1-1. Search in Google Scholar

[21] Wang, H., Ruan, J., Zhou, B., Li, C. B., Wu, Q., & Raza, M. Q., et al. (2019). Dynamic data injection attack detection of cyber-physical power systems with uncertainties. IEEE Transactions on Industrial Informatics, 1-1. Search in Google Scholar