Home   >   CSC-OpenAccess Library   >    Manuscript Information
Full Text Available

(321.24KB)
This is an Open Access publication published under CSC-OpenAccess Policy.
Publications from CSC-OpenAccess Library are being accessed from over 74 countries worldwide.
Protocol Type Based Intrusion Detection Using RBF Neural Network
Aslihan Ozkaya, Bekir Karlik
Pages - 90 - 99     |    Revised - 15-07-2012     |    Published - 10-08-2012
Volume - 3   Issue - 4    |    Publication Date - December 2012  Table of Contents
MORE INFORMATION
KEYWORDS
RBF network, Intrusion Detection, Network Security, KDD dataset
ABSTRACT
Intrusion detection systems (IDSs) are very important tools for providing information and computer security. In IDSs, the publicly available KDD’99, has been the most widely deployed data set used by researchers since 1999. Using a common data set has been provided to compare the results of different researches. The aim of this study is to find optimal methods of preprocessing the KDD’99 data set and employ the RBF learning algorithm to apply an Intrusion Detection System.
CITED BY (2)  
1 Shrivastava, A., Baghel, M., & Gupta, H. (2013). A Novel Hybrid Feature Selection and Intrusion Detection Based On PCNN and Support Vector Machine. International Journal of Computer Technology and Applications, 4(6), 922.
2 Shrivastava, A., Baghel, M., & Gupta, H. (2013). A Review of Intrusion Detection Technique by Soft Computing and Data Mining Approach. International Journal of Advanced Computer Research, 3(3), 224.
1 Google Scholar 
2 CiteSeerX 
3 refSeek 
4 Scribd 
5 SlideShare 
6 PdfSR 
1 K. Ilgun, R.A Kemonerer and P.A Porras, “State Transition Analysis: A Rule Based Intrusion Detection Approach”, IEEE Transaction on Software Engineering, Vol.21(3),March 1995, pp.181-199.
2 S. Capkun. Levente Buttyan. “Self-Organized Public Key Management For Mobile Ad Hoc Networks”, IEEE Transactions on Mobile Computing, Vol. 2(1), January -March 2003, pp. 52-64.
3 Yao, J. T., S.L. Zhao, and L.V. Saxton, “A Study On Fuzzy Intrusion Detection”, In Proceedings of the Data Mining, Intrusion Detection, Information Assurance, and Data Networks Security, SPIE, Vol. 5812,pp. 23-30, Orlando, Florida, USA, 2005.
4 R. P. Lippmann, D. J. Fried, I. Graf, J. W. Haines, K. R. Kendall, D.McClung, D. Weber,S. E. Webster, D. Wyschogrod, R. K. Cunningham, and M. A. Zissman, “Evaluating Intrusion Detection Systems: The 1998 DARPA Off-Line Intrusion Detection Evaluation,” in Proc. DARPA Inf. Survivability Confer. Exposition (DISCEX), Vol. 2, 2000, pp. 12–26.
5 M. Tavallaee, E. Bagheri, W. Lu, and A. Ghorbani, “A Detailed Analysis of the KDD CUP 99 Data Set,” in Proc. 2009 IEEE International Conference on Computational Intelligence for Security and Defense Applications. pp. 53-58.
6 M. Sabhnani and G. Serpen, “Application of Machine Learning Algorithms to KDD 1999 Cup Intrusion Detection Dataset within Misuse Detection Context”, International Conference on Machine Learning, Models, Technologies and Applications Proceedings, Las Vegas, Nevada, June 2003, pp. 209-215.
7 J. Bi, K. Zhang, X. Cheng, “Intrusion Detection Based on RBF Neural Network”, Information Engineering and Electronic Commerce, 2009, pp. 357 - 360
8 S.Sagiroglu, E. N. Yolacan, U. Yavanoglu, “Designing and Developing an Intelligent Intrusion Detection System”, Journal of the Faculty of Engineering and Architecture of Gazi University, Vol. 26 (2), June 2011, pp. 325-340.
9 Yu Y, and Huang Hao, “An Ensemble Approach to Intrusion Detection Based on Improved Multi-Objective Genetic Algorithm”, Journal of Software, Vol.18 (6), June 2007,pp.1369-1378.
10 O. Adetunmbi Adebayo, Zhiwei Shi, Zhongzhi Shi, Olumide S. Adewale, “Network Anomalous Intrusion Detection using Fuzzy-Bayes", IFIP International Federation for Information Processing, Vol. 228, 2007, pp. 525-530.
11 R. Shanmugavadivu and N. Nagarajan, “An Anomaly-Based Network Intrusion Detection System Using Fuzzy Logic”, International Journal of Computer Science and Information Security, Vol. 8 (8), November 2010, pp. 185-193.
12 U. Ahmed and A. Masood, “Host Based Intrusion Detection Using RBF Neural Networks”,Emerging Technologies, ICET 2009, 19-20 Oct. 2009, pp. 48-51.
13 The UCI KDD Archive, University of California, KDD Cup 1999 Data,http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html, October 28, 1999, [Feb2012].
14 J. Mark and L. Orr, “Introduction to Radial Basis Function Networks”, Technical Report,April 1996.
15 Z. Caiqing, Q. Ruonan, and Q. Zhiwen, “Comparing BP and RBF Neural Network for Forecasting the Resident Consumer Level by MATLAB,” International Conference on Computer and Electrical Engineering, 2008 (ICCEE 2008), 20-22 Dec. 2008, pp.169-172.
16 A. Iseri and B. Karlik, “An Artificial Neural Networks Approach on Automobile Pricing”,Expert Systems with Applications, Vol. 36 (2), March 2010, pp. 2155-2160.
Miss Aslihan Ozkaya
Mevlana University - Turkey
aozkaya@mevlana.edu.tr
Professor Bekir Karlik
Mevlana University - Turkey