A research using hybrid RBF/Elman neural networks for intrusion detection system secure model

Computer Physics Communications

Volumn 180, Issue 10, 2009, Pages 1795-1801

  Tong, Xiaojun ^a   Wang, Zhu ^a   Yu, Haining ^a  

^a HARBIN INSTITUTE OF TECHNOLOGY   (China)

Author keywords

Anomaly detection; Hybrid RBF Elman neural network; Intrusion detection; Memory of events; Misuse detection

Indexed keywords

ANOMALY DETECTION; DEFENSE ADVANCED RESEARCH PROJECTS AGENCIES; DETECTION RATES; ELMAN NETWORK; FALSE POSITIVE RATES; HYBRID NEURAL NETWORKS; HYBRID RBF/ELMAN NEURAL NETWORK; INTRUSION DETECTION SYSTEMS; MEMORY OF EVENTS; MISUSE DETECTION; NEURAL NETWORK MODEL; PATTERN CLASSIFICATION; RBF NETWORK; ROC CURVES;

COMPUTER CRIME; DISTRIBUTED COMPUTER SYSTEMS; NEURAL NETWORKS;

INTRUSION DETECTION;

EID: 69349090629     PISSN: 00104655     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.cpc.2009.05.004     Document Type: Article

References (13)

1. Bivens A., Palagiri C., Smith R., Szymanski B., et al. Network-based intrusion detection using neural networks. Proceeding of ANNIE-2002 (2002), ASME Press, New York 579-584
2. Yang Ke, Wang Li-Ping, and Fang Ding-Yi. Program behavior anomaly detection based on neural network. Dalian Ligong Daxue Xuebao (Journal of Dalian University of Technology) 45 Suppl. (2005) S136-S141
3. Amini M., Jalili R., and Shahriari H.R. RT-UNNID: A practical solution to real-time network-based intrusion detection using unsupervised neural networks. Computers & Security 25 6 (2006) 459-468
4. Guisong Liu, Zhang Yi, and Shangming Yang. A hierarchical intrusion detection model based on the PCA neural networks. Neurocomputing 70 7-9 (2007) 1561-1568
5. Ghosh A.K., and Schwartzbard A. A study in using neural networks for anomaly and misuse detection. Proceedings of 8th USENIX Security Symposium (1999), USENIX Association, San Washington 23-36
6. Aparicio Acosta F.M. Radial basis function and related models: An overview. Signal Processing 45 1 (1995) 37-58
7. Kalinli A., and Sagiroglu S. Elman network with embedded memory for system identification. Journal of Information Science and Engineering 22 6 (2006) 1555-1568
8. Sun Microsystems. Sun SHIELD Basic Security Module Guide (1995). http://docs-pdf.sun.com/802-1965/802-1965.pdf.CA
9. Hofmeyr S.A., Forrest S., and Somayaji A. Intrusion detection using sequences of system calls. Journal of Computer Security 6 3 (1998) 151-180
10. R.K. Cunningham, R.P. Lippmann, D.J. Fried, et al., Evaluating intrusion detection systems without attacking your friends: The 1998 DARPA intrusion detection evaluation, in: Proceedings of Third Conference and Workshop on Intrusion Detection and Response, San Diego, CA, 1999.10-21
11. Lippmann R., Haines J.W., Fried D.J., et al. The 1999 DARPA off-line intrusion detection evaluation. Computer Networks 30 2 (2000) 14-26
12. Srinivas Mukkamala, Guadalupe Janoski, Andrew Sung, Intrusion detection using neural networks and support vector machines, in: Proceedings of the International Joint Conference on Neural Networks, vol. 2, 2002, pp. 1702-1707
13. C. Jirapummin, N. Wattanapongsakorn, P. Kanthamanon, Hybrid neural networks for intrusion detection system, in: Proceedings of the International Technical Conference on Circuits/Systems, Computers and Communications (ITC-CSCC 2002), Thailand, July 2002, pp. 928-931