AI Driven False Data Injection Attack Recognition Approach for Cyber-Physical Systems in Smart Cities

[1] Huang, X., Li, Z. and Ding, D.W., 2022. Finite-time attack detection for nonlinear complex cyber-physical networks under false data injection attacks. Journal of the Franklin Institute, 359(18), pp.10510-10524. Search in Google Scholar

[2] Chen, Y., Li, T., Long, Y. and Bai, W., 2023. Attacks Detection and Security Control for Cyber-Physical Systems under False Data Injection Attacks. Journal of the Franklin Institute. Search in Google Scholar

[3] Liu, X., Chang, P., Wu, Z., Jiang, M. and Sun, Q., 2022. Malicious data injection attacks risk mitigation strategy of cyber–physical power system based on hybrid measurements attack detection and risk propagation. International Journal of Electrical Power & Energy Systems, 142, p.108241. Search in Google Scholar

[4] Lu, K.D. and Wu, Z.G., 2022. Multi-objective false data injection attacks of cyber–physical power systems. IEEE Transactions on Circuits and Systems II: Express Briefs, 69(9), pp.3924-3928. Search in Google Scholar

[5] Hu, Y., Zhu, P., Xun, P., Liu, B., Kang, W., Xiong, Y. and Shi, W., 2021. CPMTD: Cyber-physical moving target defense for hardening the security of power system against false data injected attack. Computers & Security, 111, p.102465. Search in Google Scholar

[6] Tian, J., Wang, B., Li, J. and Konstantinou, C., 2022. Datadriven false data injection attacks against cyber-physical power systems. Computers & Security, 121, p.102836. Search in Google Scholar

[7] Bhattar, P.L., Pindoriya, N.M. and Sharma, A., 2021. A combined survey on distribution system state estimation and false data injection in cyber-physical power distribution networks. IET Cyber-Physical Systems: Theory & Applications, 6(2), pp.41-62. Search in Google Scholar

[8] Habib, A.A., Hasan, M.K., Alkhayyat, A., Islam, S., Sharma, R. and Alkwai, L.M., 2023. False data injection attack in smart grid cyber physical system: Issues, challenges, and future direction. Computers and Electrical Engineering, 107, p.108638. Search in Google Scholar

[9] Li, H., Xia, Y., Ke, J., Lv, T., Zhang, H., Zhong, Z. and Zhang, J., 2023. False data injection attacks detection based on Laguerre function in nonlinear Cyber-Physical systems. Internet Technology Letters, 6(3), p.e399. Search in Google Scholar

[10] Qu, Z., Dong, Y., Qu, N., Li, H., Cui, M., Bo, X., Wu, Y. and Mugemanyi, S., 2021. False data injection attack detection in power systems based on cyber-physical attack genes. Frontiers in Energy Research, 9, p.644489. Search in Google Scholar

[11] Alamro, H., Mahmood, K., Aljameel, S.S., Yafoz, A., Alsini, R. and Mohamed, A., 2023. Modified Red Fox Optimizer with Deep Learning enabled False Data Injection Attack Detection. IEEE Access. Search in Google Scholar

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

[13] Hallaji, E., Razavi-Far, R., Wang, M., Saif, M. and Fardanesh, B., 2022. A stream learning approach for real-time identification of false data injection attacks in cyber-physical power systems. IEEE Transactions on Information Forensics and Security, 17, pp.3934-3945. Search in Google Scholar

[14] Cao, G., Gu, W., Lou, G., Sheng, W. and Liu, K., 2022. Distributed synchronous detection for false data injection attack in cyber-physical microgrids. International Journal of Electrical Power & Energy Systems, 137, p.107788. Search in Google Scholar

[15] Zhang, G., Li, J., Bamisile, O., Cai, D., Hu, W. and Huang, Q., 2021. Spatio-temporal correlation-based false data injection attack detection using deep convolutional neural network. IEEE Transactions on Smart Grid, 13(1), pp.750-761. Search in Google Scholar

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

[17] Yang, J., 2021. A controllable false data injection attack for a cyber physical system. IEEE Access, 9, pp.6721-6728. Search in Google Scholar

[18] Xue, W. and Wu, T., 2020. Active learning-based XGBoost for cyber physical system against generic AC false data injection attacks. IEEE Access, 8, pp.144575-144584. Search in Google Scholar

[19] Wang, J., Zhang, B. and Shu, L., 2023. Research on Non-Intrusive Load Recognition Method Based on Improved Equilibrium Optimizer and SVM Model. Electronics, 12(14), p.3138. Search in Google Scholar

[20] Mafarja, M., Thaher, T., Al-Betar, M.A., Too, J., Awadallah, M.A., Abu Doush, I. and Turabieh, H., 2023. Classification framework for faulty-software using enhanced exploratory whale optimizer-based feature selection scheme and random forest ensemble learning. Applied Intelligence, pp.1-43. Search in Google Scholar

[21] Karthikeyini, S., Vidhya, G., Vetriselvi, T. and Deepa, K., 2023. Heart Disease Prognosis Using DGRU with Logistic Chaos Honey Badger Optimization in IoMT Framework. Information Technology and Control, 52(2), pp.367-380. Search in Google Scholar

[22] Zhang, C., Pei, Y.H., Wang, X.X., Hou, H.Y. and Fu, L.H., 2023. Symmetric cross-entropy multi-threshold color image segmentation based on improved pelican optimization algorithm. PloS one, 18(6), p.e0287573. Search in Google Scholar