CFP last date
20 December 2024
Call for Paper
January Edition
IJCA solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 20 December 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

Anomaly Intrusion Detection based on a Hybrid Classification Algorithm (GSVM)

by Salima Benqdara
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 181 - Number 36
Year of Publication: 2019
Authors: Salima Benqdara
10.5120/ijca2019918324

Salima Benqdara . Anomaly Intrusion Detection based on a Hybrid Classification Algorithm (GSVM). International Journal of Computer Applications. 181, 36 ( Jan 2019), 10-15. DOI=10.5120/ijca2019918324

@article{ 10.5120/ijca2019918324,
author = { Salima Benqdara },
title = { Anomaly Intrusion Detection based on a Hybrid Classification Algorithm (GSVM) },
journal = { International Journal of Computer Applications },
issue_date = { Jan 2019 },
volume = { 181 },
number = { 36 },
month = { Jan },
year = { 2019 },
issn = { 0975-8887 },
pages = { 10-15 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume181/number36/30265-2019918324/ },
doi = { 10.5120/ijca2019918324 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:08:19.034962+05:30
%A Salima Benqdara
%T Anomaly Intrusion Detection based on a Hybrid Classification Algorithm (GSVM)
%J International Journal of Computer Applications
%@ 0975-8887
%V 181
%N 36
%P 10-15
%D 2019
%I Foundation of Computer Science (FCS), NY, USA
Abstract

One of the major problems in support vector machines (SVM) is the selection of optimal parameters that can establish an efficient SVM to achieve better output with an acceptable level of accuracy. In this paper, proposed a hybrid classification algorithm (GSVM) based Gravitational Search Algorithm (GSA) and support vector machines (SVM) to optimize the accuracy of the SVM classifier by detecting the subset of the best values of the kernel parameters for the SVM classifier. In the GSVM classifier, the GSA is introduced as an optimization technique to optimize the SVM parameters. The GSVM algorithm evaluated using KDD CUP 99 data set and compared to the outperformance of the original SVM algorithms. The results show that the performance of GSVM algorithm has a higher detection rate with lower false positive rate.

References
  1. Dastanpour, A., Ibrahim, S., Mashinchi, R. and Selamat, A.," Using Gravitational Search Algorithm to Support Artificial Neural Network in Intrusion Detection System", SmartCR, VOL4 (6), (2014), 426–434.
  2. Hongying Zheng.,"An Efficient Hybrid Clustering-PSO Algorithm for Anomaly Intrusion Detection", JOURNAL OF SOFTWARE, VOL 6(12), (2011), 306-313.
  3. Kuang, F., Xu, W. and Zhang, S., "A novel hybrid KPCA and SVM with GA model for intrusion detection", Applied Soft Computing. VOL.18, (2014), 178–184.
  4. Manekar, V. and Waghmare, K. ,"Intrusion Detection System using Support Vector Machine (SVM) and Particle Swarm Optimization (PSO) ", International Journal of Advanced Computer Research, VOL 4(3), (2014),25–30.
  5. Mukkamala, S., Sung A. and Abraham, A. (2003). Intrusion Detection Using Ensemble of Soft Computing Paradigms. Proceedings of 3rd. International Conference on Intelligent Systems Design and Applications. 239-248.
  6. Rashedi,E., Nezamabadi,H. and Saryazdi, S., "GSA: A gravitational search algorithm", Information Sciences, VOL. 179,(2009), 2232-2248.
  7. Peddabachigari, S. Abraham, A. Grosan, C. J., "Thomas: Modeling intrusion detection system using hybrid intelligent systems", In Journal of Network and Computer Applications, VOL. 30, (2007), 114-132.
  8. Shih.W, Kuo.C , Shih.C and Zne. L. , "Particle swarm optimization for parameter determination and feature selection of support vector machines", Expert Systems with Applications,(2008) 1817–1824.
  9. Tsai, C., Hsu, Y., Lin, C. and Lin, W. , " Intrusion Detection by Machine Learning: A Review", Expert Systems with Applications. VOL 36(10), (2009)11994-12000.
  10. Ranaee, V., Ebrahimizadeh, A., Ghaderi, R., "Application of the PSO–SVM model for recognition of control chart patterns", ISA Transactions, VOL 49 (4), (2010), 577–586.
  11. Vapnik, V., "Statistical learning theory", Wiley, New York, (1998).
  12. Wang, J., Li, T. and Ren, R. (2010b). A real Time IDS Based on Artificial Bee Colony-Support Vector Machine Algorithm. In The Third International Workshop 133 on Advanced Computational Intelligence (IWACI). IEEE, 91–96.
  13. Manikandan, R., Oviya, P. and Hemalatha, C.,"A New Data Mining Based Network Intrusion Detection Model. Journal of Computer Application", VOL. 5, (2012), 1–10.
  14. Tsai, C.-F. and Lin, C.-Y. , "A triangle Area based Nearest Neighbors Approach to Intrusion Detection", Pattern Recognition. VOL 43(1), (2010), 222–229.
  15. Majid, A., Khan, A. and Mirza, A. M., "Combination of support vector machines using genetic programming", International Journal of Hybrid Intelligent Systems. VOL3 (2), (2006), 109–125.
  16. Srinivas, M. and Andrew, H., "Feature selection for intrusion detection using neural networks and support vector machines", Transportation Research Board, winter, (2003), 1–11.
  17. Tsai, C.-F., Hsu, Y.-F., Lin, C.-Y, and Lin, W.-Y, "Intrusion Detection by Machine Learning: A review ", Expert Systems with Applications, VOL 36 (10), (2009), 11994– 12000.
  18. Saini, G. and Kaur, H. , "A Novel Approach Towards K-Mean Clustering Algorithm With PSO", International Journal of Computer Science and Information Technologies, VOL 5(4), (2014), 5978–5986.
Index Terms

Computer Science
Information Sciences

Keywords

Network Intrusion Detection ensemble clusters unlabeled data.

Powered by PhDFocusTM