Abstract
With the rapid development of the network, network security has become one of the most important issues, as it directly affects the interests of the state, enterprises and individuals. Further more, with the increasingly mature of the attack knowledge, complex and diverse attack tools and techniques, the current simple firewall technology has been unable to meet the needs of the people. Facing so many challenges and threats, intrusion detection and prevention technology is bound to become one of the core technologies in the security audit. The iintrusion detection, playing a role of active defense, is an effective complement to the firewall, and is an important part of network security. This paper mainly analyzes the decision tree algorithm and improved Naive Bayes algorithm, proving the effectiveness of the improved Naive Bayes algorithm.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Peddabachigari, S., Abraham, A., Grosan, C., Thomas, J.: Modelling Intrusion Detection System Using Hybrid Systems. J. Network Comput. Appl. 30, 114–132 (2007)
Xiang, C., Chong, M.Y., Zhu, H.L.: Design of Multiple-level Tree Classifiers for Intrusion Detection System. In: Proc.2004 IEEE Conf. on Cybernetics and Intelligent Systems, Singapore, pp. 872–877 (December 2007)
Xiang, C., Yong, P.C., Meng, L.S.: Design of Multiple-level Hybrid Classifier for Intrusion Detection System Using Bayesian Clustering and Decision Trees. Pattern Recognition Letters 29, 918–924 (2008)
Kumar, S., Spafford, E.H.: Software Architecture to Support Misuse Intrusion Detection. In: Proceedings of the 18th National Information Security Conference, pp. 194–204 (2005)
Smaha, S.E.: Haystack: An Intrusion Detection System. In: Proceedings of the IEEE Fourth Aerospace Computer Security Applications Conference, Orlando, FL, pp. 37–44 (2008)
Lee, W.: A Data Mining for Constructing Features and Models for Intrusion Detection System, Ph. D. Dissertation, Columbia University (2009)
Lee, W., Stolfo, S.J.: Data Mining Approaches for Intrusion Detection. In: Proc. the 7th USENIX Security Symposium, San Antonio, TX (2008)
Pfahringer, B.: Winning the KDD99 Classification Cup: Bagged Boosting. In: SIGKDD Explorations, 2000 ACM SIGKDD, vol. 1(2), pp. 65–66 (January 2000)
Kruegel, C., Mutz, D., Robertson, W., Valeur, F.: Bayesian Event Classification for Intrusion Detection. In: Proceedings of the 19th Annual Computer Security Applications Conference, Las Vegas, NV (2007)
Valdes, A., Skinner, K.: Adaptive Model-based Monitoring for Cyber Attack Detection. In: Recent Advances in Intrusion Detection, Toulouse, France, pp. 80–92 (2008)
Ye, N., Xu, M., Emran, S.M.: Probabilistic Networks with Undirected Links for Anomaly Detection. In: Proceedings of the IEEE Systems, Man and Cybernetics Information Assurance and Security Workshop, West Point, NY (2009)
Portnoy, L., Eskin, E., Stolfo, S.J.: Intrusion Detection with Unlabeled Data Using Clustering. In: Proceedings of the ACM Workshop on Data Mining Applied to Security, Philadelphia, PA (2007)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Yunfeng, Y. (2013). Research on the System Model of Network Intrusion Detection. In: Du, Z. (eds) Proceedings of the 2012 International Conference of Modern Computer Science and Applications. Advances in Intelligent Systems and Computing, vol 191. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33030-8_30
Download citation
DOI: https://doi.org/10.1007/978-3-642-33030-8_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33029-2
Online ISBN: 978-3-642-33030-8
eBook Packages: EngineeringEngineering (R0)