Computer Security Active Defense Technology Based on Bayesian Model

[1] Alexander, R. (2020). Reducing Threats by Using Bayesian Networks to Prioritize and Combine Defense in Depth Security Measures. Journal of Information Security, 11(3), 121-137. Search in Google Scholar

[2] Hu, H., Liu, J., Tan, J., & Liu, J. (2020). SOCMTD: selecting optimal countermeasure for moving target defense using dynamic game. KSII Transactions on Internet and Information Systems (TIIS), 14(10), 4157-4175. Search in Google Scholar

[3] Khakzad, N. (2021). A Tutorial on Fire Domino Effect Modeling Using Bayesian Networks. Modelling, 2(2), 240-258. Search in Google Scholar

[4] Zheng, Y., Hu, X., & Sun, H. (2021). Research on Motion Control for a Mobile Robot Using Learning Control Method. Applied Mathematics and Nonlinear Sciences, 6(1), 227-234. Search in Google Scholar

[5] Rajesh Kanna, M., Pradeep Kumar, R., Nandappa, S., & Cangul, I. (2020). On Solutions of Fractional order Telegraph Partial Differential Equation by Crank-Nicholson Finite Difference Method. Applied Mathematics and Nonlinear Sciences, 5(2), 85-98. Search in Google Scholar

[6] Wang, X., Bryan, C., Li, Y., Pan, R., Liu, Y., Chen, W., & Ma, K. L. (2020). Umbra: a visual analysis approach for defense construction against inference attacks on sensitive information. IEEE Transactions on Visualization and Computer Graphics, 28(7), 2776-2790. Search in Google Scholar

[7] Mitchell, E. G., Whittle, R. J., & Griffiths, H. J. (2020). Benthic ecosystem cascade effects in Antarctica using Bayesian network inference. Communications Biology, 3(1), 1-7. Search in Google Scholar

[8] Wu, W., & Yang, C. Y. (2022). An Overview on Network Security Situation Awareness in Internet. International Journal of Network Security, 24(3), 450-456. Search in Google Scholar

[9] Zhu, M., Anwar, A. H., Wan, Z., Cho, J. H., Kamhoua, C. A., & Singh, M. P. (2021). A survey of defensive deception: Approaches using game theory and machine learning. IEEE Communications Surveys & Tutorials, 23(4), 2460-2493. Search in Google Scholar

[10] Zhao, F., Zhang, H., Peng, J., Zhuang, X., & Na, S. G. (2020). A semi-self-taught network intrusion detection system. Neural Computing and Applications, 32(23), 17169-17179. Search in Google Scholar

[11] Khowaja, S. A., & Khuwaja, P. (2021). Q-learning and LSTM based deep active learning strategy for malware defense in industrial IoT applications. Multimedia Tools and Applications, 80(10), 14637-14663. Search in Google Scholar