CFP last date
22 April 2024
Call for Paper
May Edition
IJCA solicits high quality original research papers for the upcoming May edition of the journal. The last date of research paper submission is 22 April 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

Hybrid Intrusion Detection System using FCRM Mechanism

by P.ananthi, P.balasubramanie
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 105 - Number 9
Year of Publication: 2014
Authors: P.ananthi, P.balasubramanie
10.5120/18406-9676

P.ananthi, P.balasubramanie . Hybrid Intrusion Detection System using FCRM Mechanism. International Journal of Computer Applications. 105, 9 ( November 2014), 25-29. DOI=10.5120/18406-9676

@article{ 10.5120/18406-9676,
author = { P.ananthi, P.balasubramanie },
title = { Hybrid Intrusion Detection System using FCRM Mechanism },
journal = { International Journal of Computer Applications },
issue_date = { November 2014 },
volume = { 105 },
number = { 9 },
month = { November },
year = { 2014 },
issn = { 0975-8887 },
pages = { 25-29 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume105/number9/18406-9676/ },
doi = { 10.5120/18406-9676 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:37:17.069499+05:30
%A P.ananthi
%A P.balasubramanie
%T Hybrid Intrusion Detection System using FCRM Mechanism
%J International Journal of Computer Applications
%@ 0975-8887
%V 105
%N 9
%P 25-29
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The necessity of efficient intrusion detection system increased recent research to be focused on hybrid techniques for better results. In recent research plenty of intrusion detection systems have been proposed with various data mining techniques, machine learning mechanisms and fuzzy logic. Existing intrusion detection systems suffered from higher false positive rate and negative rate. This paper proposes the integrated approach such as clustering with Fuzzy neural network for efficient detection rate. In this proposed approach, Fuzzy C-Regression technique is used to construct different training subsets. Then, FNN model is used to take decision making. This proposed approach significantly reduces the false positive and negative rate.

References
  1. S. -X. Wu and W. Banzhaf, 2010. The Use of Computational Intelligence in Intrusion Detection Systems: A Review. Elsevier Applied Soft Computing, vol. 10(1), pp. 1–35.
  2. H. T. Elshoush and I. M. Osman, 2000. Reducing False Positives through Fuzzy Alert Correlation in Collaborative Intelligent Intrusion Detection Systems — A Review. IEEE Int'l. Conf. Fuzzy Systems, pp. 1–8.
  3. Patcha, A. , & Park, J. M. 2007. An overview of anomaly detection techniques: Existing solutions and latest technological trends. Computer Networks, 51(12), 3448–3470.
  4. Manikopoulos, C. , & Papavassiliou, S. 2002. Network intrusion and fault detection: A statistical anomaly approach. IEEE Communications Magazine, 40(10), 76–82.
  5. Ryan, J. , Lin, M. , & Miikkulainen, R. 1998. Intrusion detection with neural networks. Advances in neural information processing systems (Vol. 10). Cambridge, MA: Springer.
  6. P. Spathoulas and S. K. Katsikas, 2009. Using a Fuzzy Inference System to Reduce False Positives in Intrusion Detection. Proc. 16th Int'l. Conf. Systems, Signals and Image Processing.
  7. Kosko, Bart, 1992. Neural Networks and Fuzzy Systems: A Dynamical Systems Approach to Machine Intelligence. Englewood Cliffs, NJ: Prentice Hall. ISBN 0-13-611435-0.
  8. Lin, W. J. Hwang, and R. J. Wai, 1999. A supervisory fuzzy neural network control system for tracking periodic inputs. IEEE Trans. Fuzzy Systems, Volume 7, No. 1, pp. 41-52.
  9. Y. C. Chen and C. C. Teng, 1995. A model reference control structure using a fuzzy neural network. Fuzzy Sets and Systems, Volume 73, pp. 291-312.
  10. Bezdek, J. 1974. Fuzzy mathematics in pattern classification. Ph. D. thesis. Ithaca, NY: Cornell University.
  11. Beghdad, R. 2008. Critical study of neural networks in detecting intrusions. Computers and Security, 27(5-6), 168–175.
  12. Axelsson, S. 2003. The base-rate fallacy and the difficulty of intrusion detection. ACM Transaction on Information and System Security, 3, 186–205.
  13. Hathaway, R. J. , Bezdek, J. C. , 1993. Switching regression models and fuzzy clustering. IEEE Trans. Fuzzy Syst. 1 (3), 195–204.
  14. Moez Solutani, Abdelkadar Chaary, Faycal Benhimda, 2012. A Novel Fuzzy C-regression Model using a new error measure and particle swarm optimization. International Journal of applied Mathematics and computer science, 22(3), 617-628.
  15. Jiang J, Zhang C, Kame M. 2003. RBF-based real-time hierarchical intrusion detection systems. In Pro-ceedings of the International Joint Conference on Neural Networks (IJCNN'03), vol. 2, pp. 1512–1516.
  16. M. Tavallaee, E. Bagheri, W. Lu, and A. Ghorbani. 2009. A Detailed Analysis of the KDD CUP 99 Data Set. Second IEEE Symposium on Computational Intelligence for Security and Defense Applications (CISDA).
  17. Yang Li, Li Guo, 2007. An active learning based TCM-KNN algorithm for supervised network intrusion detection, Computers & Security 26 , 459–467
  18. Wenying Feng, Qinglei Zhang, Gongzhu Hu, Jimmy Xiangji Huang, Mining network data for intrusion detection through combiningSVMs with ant colony networks,Future Generation Computer Systems 37,127–140
  19. Dr. Saurabh Mukherjee,, Neelam Sharma, 2014. Intrusion Detection using Naive Bayes Classifier with Feature Reduction Procedia Technology 4, 119 – 128, Elsevier
  20. Norbik Bashah, Idris Bharanidharan Shanmugam, and Abdul Manan Ahmed,2005. Hybrid Intelligent Intrusion Detection System. World Academy of Science, Engineering and Technology.
  21. Gan Xu-sheng, Duanmu Jing-shun, Wang Jia-fu, Cong Wei, 2013. Anomaly intrusion detection based on PLS feature extraction and core vector machine, Knowledge-Based Systems 40, 1–6, Elsevier.
  22. Hsu-Kun Wu, Jer-Guang Hsieh, Yih-Lon Lin, Jyh-Horng Jeng, 2010. On maximum likelihood fuzzy neural networks, Fuzzy Sets and Systems 161, 2795 – 2807, Science Direct.
  23. Michel Menard, 2001. Fuzzy clustering and switching regression models using ambiguity, and distance rejects, Fuzzy Sets and Systems 122, 363–399, Elsevier
  24. I. B. Türk?en, 2011. A review of developments in fuzzy system models: Fuzzy rule bases to fuzzy functions Scientia Iranica D 18 (3), 522–527.
  25. Heba F. Eid, Ashraf Darwish, Aboul Ella Hassanien, and Ajith Abraham, 2010. Principle Components Analysis and Support Vector Machine based Intrusion Detection System 978-1-4244-8136, IEEE.
  26. Chaoshun Li, Jianzhong Zhou , Xiuqiao Xiang, Qingqing Li, Xueli An, 2009. T–S fuzzy model identification based on a novel fuzzy c-regression model clustering algorithm, Engineering Applications of Artificial Intelligence, 646–653.
Index Terms

Computer Science
Information Sciences

Keywords

Intrusion Detection System Fuzzy Neural Network Fuzzy C-Regression model false positive

Powered by PhDFocusTM