AI Driven False Data Injection Attack Recognition Approach for Cyber-Physical Systems in Smart Cities
References
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- Yang, J., 2021. A controllable false data injection attack for a cyber physical system. IEEE Access, 9, pp.6721-6728.
- 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.
- 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.
- 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.
- 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.
- 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.
DOI: https://doi.org/10.2478/jsiot-2023-0008 | Journal eISSN: 2956-8323
Language: English
Page range: 13 - 32
Submitted on: Nov 1, 2023
Accepted on: Nov 20, 2023
Published on: Dec 15, 2023
Published by: Future Sciences For Digital Publishing
In partnership with: Paradigm Publishing Services
Publication frequency: 2 issues per year
Keywords:
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© 2023 Pooja Joshi, Anurag Sinha, Roumo Kundu, Rejuwan Shamim, Mukesh Kumar Bagaria, Yuvraj Singh Rajawat, Piyush Punia, published by Future Sciences For Digital Publishing
This work is licensed under the Creative Commons Attribution 4.0 License.