Open Access
Issue |
ITM Web Conf.
Volume 56, 2023
First International Conference on Data Science and Advanced Computing (ICDSAC 2023)
|
|
---|---|---|
Article Number | 06003 | |
Number of page(s) | 7 | |
Section | Networks & Information Security | |
DOI | https://doi.org/10.1051/itmconf/20235606003 | |
Published online | 09 August 2023 |
- F. Zhang, H. A. D. E. Kodituwakku, J. W. Hines, and J. Coble, “Multilayer Data-Driven Cyber-Attack Detection System for Industrial Control Systems Based on Network, System, and Process Data,” IEEE Transactions on Industrial Informatics, vol. 15, no. 7, pp. 4362-4369, (2019). [CrossRef] [Google Scholar]
- R. Ma, P. Cheng, Z. Zhang, W. Liu, Q. Wang, and Q. Wei, “Stealthy Attack Against Redundant Controller Architecture of Industrial Cyber Physical System,” IEEE Internet of Things Journal, vol. 6, no. 6, pp. 9783-9793, (2019). [CrossRef] [Google Scholar]
- G. Falco, C. Caldera, and H. Shrobe, “IIoT Cybersecurity Risk Modeling for SCADA Systems,” IEEE Internet of Things Journal, vol. 5, no. 6, pp. 4486-4495, (2018). [CrossRef] [Google Scholar]
- J. Yang, C. Zhou, S. Yang, H. Xu, and B. Hu, “Anomaly Detection Based on Zone Partition for Security Protection of Industrial Cyber-Physical Systems,” IEEE Transactions on Industrial Electronics, vol. 65, no. 5, pp. 4257-4267, (2018). [CrossRef] [MathSciNet] [Google Scholar]
- S. Ponomarev and T. Atkison, “Industrial control system network intrusion detection by telemetry analysis,” IEEE Transactions on Dependable and Secure Computing, vol. 13, no. 2, pp. 252-260, (2016). [CrossRef] [Google Scholar]
- J.F. Clemente, “No cybersecurity for critical energy infrastructure,” Ph.D. dissertation, Naval Postgraduate School, (2018). [Google Scholar]
- C. Bellinger, S. Sharma, and N. Japkowicz, “One class versus binary classification: Which and when in 2012 11th International Conference on Machine Learning and Applications, vol. 2, 2012, pp. 102-106. [CrossRef] [Google Scholar]
- Y. Bengio, A. Courville, and P. Vincent, “Representation learning: A review and new perspectives,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 8, pp. 1798-1828, (2013). [CrossRef] [Google Scholar]
- M. Zolanvari, M. A. Teixeira, L. Gupta, K. M. Khan, and R. Jain, “Machine Learning- Based Network Vulnerability Analysis of Industrial Internet of Things,” IEEE Internet of Things Journal, vol. 6, no. 4, pp. 6822-6834, (2019). [CrossRef] [Google Scholar]
- I. A. Khan, D. i, Z. U. Khan, Y. Hussain, and A. Nawaz, “HML-IDS: A hybrid- multilevel anomaly prediction approach for intrusion detection in SCADA systems,” IEEE Access, vol. 7, pp. 89507-89521, (2019). [CrossRef] [Google Scholar]
- T. K. Das, S. Adepu, and J. Zhou, “Anomaly detection in industrial control systems using logical analysis of data,” Computers & Security, vol. 96, p. 101935, (2020). [CrossRef] [Google Scholar]
- J. J. Q. Yu, Y. Hou, and V. O. K. Li, “Online False Data Injection Attack Detection With Wavelet Transform and Deep Neural Networks,” IEEE Transactions on Industrial Informatics, vol. 14, no. 7, pp. 3271-3280, (2018). [CrossRef] [Google Scholar]
- M. M. N. Aboelwafa, K. G. Seddik, M. H. Eldefrawy, Y. Gadallah, and M. Gidlund, “A machine learning-based technique for false data injection attacks detection in industrial iot,” IEEE Internet of Things Journal, vol. 7, no. 9, pp. 8462-8471, (2020) [CrossRef] [Google Scholar]
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