CFP last date
20 January 2025
Call for Paper
February Edition
IJCA solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 20 January 2025

Submit your paper
Know more
The week's pick
Responsive image
SFLA-Based Line Balancing and Task Optimization in Master-Slave Controlled Hanger Transportation Systems for Garment Production

Duc Hoang Nguyen
Random Articles
Reseach Article

A Novel Cluster-based Intrusion Detection Approach Integrating Multiple Learning Techniques

by Hossein Shapoorifard, Pirooz Shamsinejad
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 166 - Number 3
Year of Publication: 2017
Authors: Hossein Shapoorifard, Pirooz Shamsinejad
10.5120/ijca2017913948

Hossein Shapoorifard, Pirooz Shamsinejad . A Novel Cluster-based Intrusion Detection Approach Integrating Multiple Learning Techniques. International Journal of Computer Applications. 166, 3 ( May 2017), 13-16. DOI=10.5120/ijca2017913948

@article{ 10.5120/ijca2017913948,
author = { Hossein Shapoorifard, Pirooz Shamsinejad },
title = { A Novel Cluster-based Intrusion Detection Approach Integrating Multiple Learning Techniques },
journal = { International Journal of Computer Applications },
issue_date = { May 2017 },
volume = { 166 },
number = { 3 },
month = { May },
year = { 2017 },
issn = { 0975-8887 },
pages = { 13-16 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume166/number3/27649-2017913948/ },
doi = { 10.5120/ijca2017913948 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:12:42.434658+05:30
%A Hossein Shapoorifard
%A Pirooz Shamsinejad
%T A Novel Cluster-based Intrusion Detection Approach Integrating Multiple Learning Techniques
%J International Journal of Computer Applications
%@ 0975-8887
%V 166
%N 3
%P 13-16
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In order to make computer systems completely secure, in addition to firewalls and other intrusion protection devices, other systems called intrusion detection systems (IDS) are needed to detect intrusion and provide solutions to counter the intruder if he penetrated through firewall, antivirus and other security devices. Many IDS have been developed based on machine learning techniques. Specifically, advanced detection approaches created by combining or integrating multiple learning techniques have shown better detection performance than general single learning techniques. This paper proposes an improvement for a feature representation approach, namely the cluster center and nearest neighbor (CANN) approach.

References
  1. A. Youssef and A. Emam, “Network Intrusion Detection Using Data Mining and Network Behaviour Analysis,” Int. J. Comput. …, vol. 3, no. 6, pp. 87–98, 2011.
  2. H. Jiawei, M. Kamber, J. Han, M. Kamber, and J. Pei, Data Mining: Concepts and Techniques. 2012.
  3. Y. Chen, A. Abraham, and B. Yang, “Hybrid flexible neural-tree-based intrusion detection systems,” Int. J. Intell. Syst., vol. 22, no. 4, pp. 337–352, 2007.
  4. W. C. Lin, S. W. Ke, and C. F. Tsai, “CANN: An intrusion detection system based on combining cluster centers and nearest neighbors,” Knowledge-Based Syst., vol. 78, no. 1, pp. 13–21, 2015.
  5. M. K. Siddiqui and S. Naahid, “Analysis of KDD CUP 99 Dataset using Clustering based Data Mining,” Int. J. Database Theory Appl., vol. 6, no. 5, pp. 23–34, 2013.
  6. L. Dhanabal and S. P. Shantharajah, “A Study on NSL-KDD Dataset for Intrusion Detection System Based on Classification Algorithms,” Int. J. Adv. Res. Comput. Commun. Eng., vol. 4, no. 6, pp. 446–452, 2015.
  7. A. Taheri and M. Shamsfard, “Mapping farsnet to suggested upper merged ontology,” in Asia Information Retrieval Symposium, 2011, pp. 604–613.
  8. M. H. Dashtban and P. Moradi, “A novel and robust approach for iris segmentation,” Int. J. Comput., 2011.
  9. M. Dashtban and M. Balafar, “Gene selection for microarray cancer classification using a new evolutionary method employing artificial intelligence concepts,” Genomics, 2017.
  10. S. Olyaee, Z. Dashtban, M. H. Dashtban, and A. Najibi, “Hybrid analytical-neural network approach for nonlinearity modeling in modified super-heterodyne nano-metrology system,” in Telecommunications (ConTEL), Proceedings of the 2011 11th International Conference on, 2011, pp. 525–530.
  11. A. Taheri and M. Shamsfard, “SBUEI: results for OAEI 2012,” in Proceedings of the 7th International Conference on Ontology Matching-Volume 946, 2012, pp. 189–196.
  12. M. H. Dashtban, Z. Dashtban, and H. Bevrani, “A novel approach for vehicle license plate localization and recognition,” Int. J. Comput. Appl., vol. 26, no. 11, 2011.
  13. S. Olyaee, Z. Dashtban, and M. H. Dashtban, “Design and implementation of super-heterodyne nano-metrology circuits,” Front. Optoelectron., vol. 6, no. 3, pp. 318–326, 2013.
  14. M. A. Aydin, A. H. Zaim, and K. G. Ceylan, “A hybrid intrusion detection system design for computer network security,” Comput. Electr. Eng., vol. 35, no. 3, pp. 517–526, 2009.
  15. R. M. Elbasiony, E. A. Sallam, T. E. Eltobely, and M. M. Fahmy, “A hybrid network intrusion detection framework based on random forests and weighted k-means,” Ain Shams Eng. J., vol. 4, no. 4, pp. 753–762, 2013.
  16. B. M. Aslahi-Shahri et al., “A hybrid method consisting of GA and SVM for intrusion detection system,” Neural Comput. Appl., vol. 27, no. 6, pp. 1669–1676, 2016.
  17. Z. Muda, W. Yassin, M. N. Sulaiman, and N. I. Udzir, “Intrusion detection based on K-means clustering and OneR classification,” in Proceedings of the 2011 7th International Conference on Information Assurance and Security, IAS 2011, 2011, pp. 192–197.
  18. G. Wang, J. Hao, J. Ma, and L. Huang, “A new approach to intrusion detection using Artificial Neural Networks and fuzzy clustering,” Expert Syst. Appl., vol. 37, no. 9, pp. 6225–6232, 2010.
  19. G. Kim, S. Lee, and S. Kim, “A novel hybrid intrusion detection method integrating anomaly detection with misuse detection,” Expert Syst. Appl., vol. 41, no. 4 PART 2, pp. 1690–1700, 2014.
  20. V. Golmah, “An Efficient Hybrid Intrusion Detection System based on C5. 0 and SVM.,” Int. J. Database Theory Appl., vol. 7, no. 2, pp. 59–70, 2014.
  21. A. P. Muniyandi, R. Rajeswari, and R. Rajaram, “Network anomaly detection by cascading k-Means clustering and C4.5 decision tree algorithm,” in Procedia Engineering, 2012, vol. 30, pp. 174–182.
  22. A. S. Eesa, Z. Orman, and A. M. A. Brifcani, “A novel feature-selection approach based on the cuttlefish optimization algorithm for intrusion detection systems,” Expert Syst. Appl., vol. 42, no. 5, pp. 2670–2679, 2015.
Index Terms

Computer Science
Information Sciences

Keywords

Intrusion Detection System Data Mining Hybrid Intrusion Detection System anomaly detection cluster center nearest neighbor.

Powered by PhDFocusTM