Call for Paper - September 2022 Edition
IJCA solicits original research papers for the September 2022 Edition. Last date of manuscript submission is August 22, 2022. Read More

Improving Intrusion Detection System using PSO and SVM Algorithm

Print
PDF
 
Download Full Text
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 175 - Number 28
Year of Publication: 2020
Authors:
Shwetamaskare, Shubhadubey
10.5120/ijca2020920770

Shwetamaskare and Shubhadubey. Improving Intrusion Detection System using PSO and SVM Algorithm. International Journal of Computer Applications 175(28):7-13, October 2020. BibTeX

@article{10.5120/ijca2020920770,
	author = {Shwetamaskare and Shubhadubey},
	title = {Improving Intrusion Detection System using PSO and SVM Algorithm},
	journal = {International Journal of Computer Applications},
	issue_date = {October 2020},
	volume = {175},
	number = {28},
	month = {Oct},
	year = {2020},
	issn = {0975-8887},
	pages = {7-13},
	numpages = {7},
	url = {http://www.ijcaonline.org/archives/volume175/number28/31626-2020920770},
	doi = {10.5120/ijca2020920770},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

The new computational requirements are growing every day, and taken advantages of these services. But these networks are not fully secured a significant amount of attacks can be deployed on these networks. Therefore to secure the network from the attackers and malicious activities the proposed work is motivated to deliver enhanced IDS (intrusion detection system). That IDS is a data mining algorithm based technique for classifying the malicious patterns. In order to implement this technique the KDD CUP dataset is used. That dataset contains 41 attributes and 1 class attribute. This huge dimension can impact on the performance of IDS system. Therefore first the data processing technique is used to cleaning the data. After that the PSO (Particle swarm optimization) technique is used. Using this algorithm , rank all the attributes and select the features. The selected features are less in size means it contains 21 attributes and 1 class attribute. In this selected features the SVM algorithm is employed for classifying the data. The experimental results on different size of dataset demonstrate the effective performance of the proposed data model. That is also compared with the relevant k-NN classification model. The comparative performance analysis demonstrate the proposed model is accurate and less time consuming for classification of patterns as compared to the k-NN based model. But the memory usages of the proposed model are higher with respect to the k-NN model.

References

  1. J. J. Jaccard, S. Nepal, “A survey of emerging threats in cybersecurity”, Journal of Computer and System Sciences, 80, 2014, 973-993
  2. C. Modi, D. Patel, H. Patel, B. Borisaniya, A. Patel, M. Rajarajan, “A survey of intrusion detection techniques in Cloud”, Journal of Network and Computer Applications, 36(1), pp. 42-57.
  3. http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html
  4. M. H. Ali, B. A. D. A. Mohammad, A. Ismail, M. F. Zolkipli, “A New Intrusion Detection System Based on Fast Learning Network and Particle Swarm Optimization”, VOLUME 6, 2018, 2169-3536, 2018 IEEE
  5. Wayne F. Cascio and Ramiro Montealegre, “How Technology Is Changing Work and Organizations”, Annual Review of Organizational Psychology and Organizational Behavior March 2016
  6. M. Kashif, S. A. Malik, M. T. Abdullah, M. Umair, P. W. Khan, “A Systematic Review of Cyber Security and Classification of Attacks in Networks”, (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 9, No. 6, 2018
  7. D. Denning, “An intrusion-detection model”, Journal of Graph Theory, SE- 13(2): pp. 222–232, 1987.
  8. B. Mukherjee, L. Heberlein, and K. Levitt, “Network intrusion detection”, Network, IEEE, 8(3): pp. 26–41, 1994.
  9. M. Joshi, “Classification, Clustering And Intrusion Detection System”, International Journal of Engineering Research and Applications (IJERA), Vol. 2, Issue 2,Mar-Apr 2012, pp.961-964
  10. V. Bande, U. D. Prasan, “Robust Intrusion Detection System using Layered Approach with Conditional Random Fields”, IJCSET, October 2011, Volume 1, Issue 9, pp. 563-568
  11. F. Gorunescu, “Data Mining: Concepts, Models, and Techniques”, Springer, 2011.
  12. J. Han, and M. Kamber, “Data mining: Concepts and techniques”, Morgan-Kaufman Series of Data Management Systems San Diego: Academic Press, 2001.
  13. N. A. Padhy, Dr. P. Mishra and R. Panigrahi, “The Survey of Data Mining Applications and Feature Scope”, International Journal of Computer Science, Engineering and Information Technology (IJCSEIT)”, vol.2, no.3, June 2012
  14. M. Rajalakshmi, M. Sakthi, “Max-Miner Algorithm Using Knowledge Discovery Process in Data Mining”, International Journal of Innovative Research in Computer and Communication Engineering, Vol. 3, Issue 11, November 2015
  15. “Data Mining Tutorial: Process, Techniques, Tools & Examples”, available online at: https://www.guru99.com/data-mining-tutorial.html
  16. D. Denning, “An intrusion-detection model. Journal of Graph Theory”, SE- 13(2): pp. 222–232, 1987.
  17. A. G. Karegowda, M. A. Jayaram, A. S. Manjunath, “Feature Subset Selection Problem using Wrapper Approach in Supervised Learning”, ©2010 International Journal of Computer Applications (0975 – 8887), Volume 1 – No. 7
  18. S. Archana and Dr. K. Elangovan, “Survey of Classification Techniques in Data Mining”, International Journal of Computer Science and Mobile Applications, Volume 2 Issue 2, February 2014.
  19. H. Jiawei, J. Pei, and M. Kamber, “Data mining: concepts and techniques”, Elsevier, 2011.
  20. V. M. Saranya and Dr. S. Uma, “Survey on Classification Techniques Used in Data Mining and their Recent Advancements”, International Journal of Science, Engineering and Technology Research, Volume 3, Issue 9, September 2014
  21. H. S. Nair, S. E. V. Ewards, “A Study on Botnet Detection Techniques”, International Journal of Scientific and Research Publications, Volume 2, Issue 4, April 2012
  22. H. A. M. Uppal, M. Javed, and M. J. Arshad, “An overview of intrusion detection system (ids) along with its commonly used techniques and classifications”, database, 19:20.
  23. V. M. Boncheva, “A short survey of intrusion detection systems”, Problems of Engineering Cybernetics and Robotics, 58:23–30, 2007
  24. V. Engen, “Machine Learning for Network Based Intrusion Detection”, June 2010, PhD. Dissertation, available online at: http://eprints.bournemouth.ac.uk/15899/1/Engen2010-PhD_single_sided.pdf
  25. M. H. Ali, B. A. D. A. Mohammad, A. Ismail, M. F. Zolkipli, “A New Intrusion Detection System Based on Fast Learning Network and Particle Swarm Optimization”, Volume 6, 2018, 2169-3536, 2018 IEEE
  26. S. Balakrishnan, K. Venkatalakshmi, “Intrusion Detection System Using Feature Selection and Classification Technique”, International Journal of Computer Science and Application, Volume 3 Issue 4, November 2014
  27. S. A. Mulay, P. R. Devale, “Intrusion Detection System using Support Vector Machine and Decision Tree”, International Journal of Computer Applications, Volume 3 – No.3, June 2010
  28. Z. Dewa, L. A. Maglaras, “Data Mining and Intrusion Detection Systems”, International Journal of Advanced Computer Science and Applications, Vol. 7, No. 1, 2016
  29. M. Srinivas, G. Janoski, A. Sung, "Intrusion detection using neural networks and support vector machines", Proceedings of the International Joint Conference on Neural Networks, IJCNN'02, Volume 2, IEEE, 2002.
  30. R. C. Chen, K. F. Cheng, “Using Rough Set and Support Vector Machine for Network Intrusion Detection”, International Journal of Network Security & Its Applications, Volume 1, No 1, April 2009
  31. C. F. Tsai, C. Y. Lin. "A triangle area based nearest neighbors approach to intrusion detection", Pattern recognition 43.1 (2010): pp. 222-229.
  32. S. M. Othman, F. M. B. Alwi, N. T. Alsohybe, A. Y. A. Hashida, “Intrusion detection model using machine learning algorithm on Big Data environment”, J Big Data (2018) 5:34, https://doi.org/10.1186/s40537-018-0145-4
  33. C. Yin, Y. Zhu, J. Fei, X. He, “A Deep Learning Approach for Intrusion Detection Using Recurrent Neural Networks”, Volume 5, 2017, 2169-3536, 2017 IEEE
  34. S. Das, A. M. Mahfouz, D. Venugopal, S. Shiva, “DDoS Intrusion Detection through Machine Learning Ensemble”, 2019 IEEE 19th International Conference on Software Quality, Reliability and Security Companion (QRS-C), 978-1-7281-3925-8/19/$31.00 ©2019 IEEE
  35. J. F. Schutte, “The Particle Swarm Optimization Algorithm”, EGM 6365 - Structural Optimization Fall 2005.

Keywords

IDS, data mining, PSO, SVM, classification, KDD CUP 99’s