Abstract
With tremendous complex attacks on the network, network analysts not only need to understand but also predict the situation of network security. In the field of network security, the research on predicting network security situation has become a hot spot. The prediction of network security situation can dynamically reflect the security situation of the entire network and provide a reliable reference to ensure the network safety. This paper predicts the network security situation using the BP and the RBF neural networks, and then makes a comparison between the two methods. The results show that the effect of the model based on the BP neural network is better than that of the model based on the RBF neural network on predicting the network security situation.
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References
Wei, Y., Lian, Y.: A Network Security Situational Awareness Model Based on Log Audit and Performance Correction. Chinese Journal of Computers 32, 763–772 (2009)
Xia, W., Wang, H.: Prediction model of network security situational based on regression analysis. In: 2010 IEEE International Conference on Wireless Communications, Networking and Information Security, WCNIS (2010)
Tang, C., Xie, Y., Qiang, B., Wang, X., Zhang, R.: Security Situation Prediction Based on Dynamic BP Neural with Covariance. In: Advanced in Control Engineering and Information Science, CEIS 2011 (2011)
Meng, J., Ma, C., He, J., Zhang, H.: Network Security Situation Prediction Model Based on HHGA-RBF Neural Network. Computer Science 38, 70 (2011)
Ren, W., Jiang, X., Sun, T.: The Prediction Method of Network Security Situation Based on RBF Neural Network. Computer Engineering and Applications 31, 136–144 (2006)
Xu, B.: Network Security Situation Prediction. Dalian University of Technology 1, 6–8 (2008)
Haykin, S.: Neural Networks, a Comprehensive Foundation, 2nd edn., pp. 161–175, 183–221,400–438. Prentice Hall (1998)
Xi, R., Jin, S., Yun, X., Zhang, Y.: CNSSA: A Comprehensive Network Security Situation Awareness System. In: 2011 IEEE 10th International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom (2011)
Onwubiko, C., Owens, T.: Situational Awareness in Computer Network Defense: Principles, Methods and Applications. Information Science Reference Press (January 2012) Ebook
Hu, W., Li, J., Chen, X., Jiang, X.: Network security situation prediction based on improved adaptive grey Verhulst model. Journal of Shanghai Jiaotong University (Science) 15(4), 408–413 (2010)
Jajodia, S.: Cyber situation awareness: issue and research (advanced in information security), Ebook (2009)
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Zhang, Y., Jin, S., Cui, X., Yin, X., Pang, Y. (2013). Network Security Situation Prediction Based on BP and RBF Neural Network. In: Yuan, Y., Wu, X., Lu, Y. (eds) Trustworthy Computing and Services. ISCTCS 2012. Communications in Computer and Information Science, vol 320. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35795-4_83
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DOI: https://doi.org/10.1007/978-3-642-35795-4_83
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-35794-7
Online ISBN: 978-3-642-35795-4
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