Acceso abierto

Application of Bayesian Network and Support Vector Machine in Evaluation and Prediction of Network Security Situation

 y   
27 feb 2025

Cite
Descargar portada

Margaritis, D., & Thrun, S. (2000). Bayesian network induction via local neighborhoods. Advances in Neural Information Processing Systems, 8(12), 505–511. MargaritisD. & ThrunS. (2000). Bayesian network induction via local neighborhoods. Advances in Neural Information Processing Systems, 8(12), 505511. Search in Google Scholar

Heckerman, D., Geiger, D., & Chickering, D. M. (1995). Learning Bayesian networks: The combination of knowledge and statistical data. Machine Learning, 20(3), 197–243. HeckermanD.GeigerD. & ChickeringD. M. (1995). Learning Bayesian networks: The combination of knowledge and statistical data. Machine Learning, 20(3), 197243. Search in Google Scholar

Heckerman, D. (1995). A tutorial on learning Bayesian networks. 61(9), 253–301. HeckermanD. (1995). A tutorial on learning Bayesian networks. 61(9), 253301. Search in Google Scholar

Chickering, D. M. (1996). Learning Bayesian networks is NP-complete. Networks, 112(2), 121–130. ChickeringD. M. (1996). Learning Bayesian networks is NP-complete. Networks, 112(2), 121130. Search in Google Scholar

Heckerman, D. (1997). Bayesian networks for data mining. Data Mining and Knowledge Discovery, 1(1), 79–119. HeckermanD. (1997). Bayesian networks for data mining. Data Mining and Knowledge Discovery, 1(1), 79119. Search in Google Scholar

Friedman, N., & Koller, D. (2003). Being Bayesian about network structure: A Bayesian approach to structure discovery in Bayesian networks. Machine Learning, 50(1/2), 95–125. FriedmanN. & KollerD. (2003). Being Bayesian about network structure: A Bayesian approach to structure discovery in Bayesian networks. Machine Learning, 50(1/2), 95125. Search in Google Scholar

Saunders, C., Stitson, M. O., Weston, J., et al. (2002). Support vector machine. Computer Science, 1(4), 1–28. SaundersC.StitsonM. O.WestonJ. et al. (2002). Support vector machine. Computer Science, 1(4), 128. Search in Google Scholar

Furey, T. S., Cristianini, N., Duffy, N., et al. (2000). Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformatics, 16(10), 906–1004. FureyT. S.CristianiniN.DuffyN. et al. (2000). Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformatics, 16(10), 9061004. Search in Google Scholar

Tong, S., & Chang, E. (2001). Support vector machine active learning for image retrieval. Conference on Proc., 23(36), 107–158. TongS. & ChangE. (2001). Support vector machine active learning for image retrieval. Conference on Proc., 23(36), 107158. Search in Google Scholar

Amari, S., & Wu, S. (1999). Improving support vector machine classifiers by modifying kernel functions. Neural Networks, 12(6), 783–789. AmariS. & WuS. (1999). Improving support vector machine classifiers by modifying kernel functions. Neural Networks, 12(6), 783789. Search in Google Scholar

Mend, J., Ma, C., & He, J., et al. (2011). Network security situation prediction model based on HHGARBF neural network prediction model of network security situation based on HHGA-RBF neural network. Computer Science, 38(7), 70–72. MendJ.MaC. & HeJ. et al. (2011). Network security situation prediction model based on HHGARBF neural network prediction model of network security situation based on HHGA-RBF neural network. Computer Science, 38(7), 7072. Search in Google Scholar

Wei, H., Li, J. H., & Chen, X. Z., et al. (2010). Network security situation prediction based on improved adaptive grey Verhulst model. Journal of Shanghai Jiao Tong University: English Version, 63(4), 6–52. WeiH.LiJ. H. & ChenX. Z. et al. (2010). Network security situation prediction based on improved adaptive grey Verhulst model. Journal of Shanghai Jiao Tong University: English Version, 63(4), 652. Search in Google Scholar

Lai, J. B., Wang, H. Q., & Liu, X. W., et al. (2008). WNN-based network security situation quantitative prediction method and its optimization. Journal of Computer Science & Technology, 4(11), 96–136. LaiJ. B.WangH. Q. & LiuX. W. et al. (2008). WNN-based network security situation quantitative prediction method and its optimization. Journal of Computer Science & Technology, 4(11), 96136. Search in Google Scholar

Qu, Z. Y., Li, Y. Y., & Li, P. (2010). A network security situation evaluation method based on D-S evidence theory. IEEE, 39(13), 326–391. QuZ. Y.LiY. Y. & LiP. (2010). A network security situation evaluation method based on D-S evidence theory. IEEE, 39(13), 326391. Search in Google Scholar

Xie, L., Wang, Y., & Yu, J. (2013). Network security situation awareness based on neural networks. Journal of Tsinghua University (Science and Technology), 53(12), 1750–1760. XieL.WangY. & YuJ. (2013). Network security situation awareness based on neural networks. Journal of Tsinghua University (Science and Technology), 53(12), 17501760. Search in Google Scholar