Research on Hybrid Neural Network in Intrusion Detection System
This paper presents an intrusion detection system of hybrid neural network model based on RBF and Elman. It is used for anomaly detection and misuse detection. This model has the memory function .It can detect discrete and related aggressive behavior effectively. RBF network is a real-time pattern classifier, and Elman network achieves the memory ability for former event. Based on the hybrid model intrusion detection system uses DARPA data set to do test evaluation. It uses ROC curve to display the test result intuitively. After the experiment it proves this hybrid model intrusion detection system can effectively improve the detection rate, and reduce the rate of false alarm and fail.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1333012Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2197
 Bivens A, Palagiri C, Smith R, Szymanski B. et al. Network-based Intrusion Detection using Neural Networks. Proceeding of ANNIE-2002, New York, ASME Press, 2002. 579-584.
 Yang Ke, Wang Li-Ping, Fang Ding-Yi. Program behavior anomaly detection based on neural network. Dalian Ligong Daxue Xuebao/Journal of Dalian University of Technology, v 45, n SUPPL., October, 2005, p S136-S141.
 Azadi Avenue, Tehran, Iran. RT-UNNID: A practical solution to real-time network-based intrusion detection using unsupervised neural networks. Computers & Security, Volume 25, Issue 6, September 2006, Pages 459-468.
 Guisong Liu, Zhang Yi and Shangming Yang. A hierarchical intrusion detection model based on the PCA neural networks Neurocomputing, Volume 70, Issues 7-9, March 2007, Pages 1561-1568.
 WeiShengJun, HuChangZhen, JiangFei. intrusion detection method (J) based on BP neural network improved algorithm. Computer engineering and application, 2005, (7) : 154-158.
 A. K. Ghosh, A. Schwartzbard. A study in using neural networks for anomaly and misuse detection (A). In Proceedings of 8th USENIX Security Symposium (C), San Washington: USENIX Association, 1999, 23-36.
 Adrian G. Bors. Introduction of the Radial Basis Function(RBF) Networks. University of York UK. : Rbf.pdf.
 LuTao, ChenDeZhao. Radial basis network research progress and review (J). Computer engineering and application, 2005, (4) : - 62.
 Elman, J.L. Finding structure in time. Cognitive Science, 1990, 14(2): 179-211.
 Sun Microsystems. Sun SHIELD Basic Security Module Guide.(BE/OL).
 S. A. Hofmeyr, S. Forrest, A. Somayaji. Intrusion detection usingsequences of system calls(J), Journal of Computer Security, 1998,(3) 151-180.
 Cunningham R K, Lippmann R P, Fried D. J, et al. Evaluating Intrusion Detection Systems without Attacking Your Friends: The 1998 DARPA Intrusion Detection Evaluation. Proceedings of Third Conference and Workshop on Intrusion Detection and Response. San Diego: CA, 1999.10-21.
 Lippmann R, Haines J W, Fried D J, et al. The 1999 DARPA Off-Line Intrusion Detection Evaluation. Computer Networks, 2000,30(2). 14-26.