Intrusion Detection Using Secured and Efficient Data Mining

Authors

  • Rashmi Chaudhari  Second Year ME, CSE, GHRIEM, North Maharashtra University, Jalgaon, Maharashtra, India
  • Sonal Patil  HOD CSE, GHRIEM, North Maharashtra University, Jalgaon, Maharashtra, India

Keywords:

KDD, NSL, Intrusion Detection.

Abstract

An Intrusion detection system (IDS) is a device or a software application that monitors a network or systems for malicious activity or policy violations. Intrusion Detection Systems supports to discover, determine, and identify unauthorized use, duplication, alteration, and destruction of information systems. So, to make intrusion detection system more efficient, the techniques that are more advantageous are used here. The NSL dataset is proposed to train the intrusion detection system. The NSL dataset is used to avoid duplication of entries of intruders in the dataset. The output received by using NSL dataset and previously proposed KDD cup dataset will be compared. Also, the real-time intrusion detection can be done.

References

  1. Anna L. Buczak, Member, IEEE, and Erhan Guven, "A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection,"ieee communications surveys and tutorials, vol. 18, no. 2, second quarter 2016.
  2. M. Bhuyan, D. Bhattacharyya, and J. Kalita, Network anomaly detection: Methods, systems and tools, IEEE Commun. Surv. Tuts., vol. 16, no. 1, pp. 303336, First Quart. 2014.
  3. T. T. T. Nguyen and G. Armitage, A survey of techniques for internet traffic classification using machine learning, IEEE Commun. Surv. Tuts., vol. 10, no. 4, pp. 5676, Fourth Quart. 2008.
  4. P. Garcia-Teodoro, J. Diaz-Verdejo, G. Maci-Fernndez, and E. Vzquez, Anomalybased network intrusion detection: Techniques, systems and challenges, Comput. Secur., vol. 28, no. 1, pp. 1828, 2009.
  5. A. Sperotto, G. Schaffrath, R. Sadre, C. Morariu, A. Pras, and B. Stiller, An overview of IP flow-based intrusion detection, IEEE Commun. Surv. Tuts., vol. 12, no. 3, pp. 343356, Third Quart. 2010.
  6. S. X. Wu and W. Banzhaf, The use of computational intelligence in intrusion detection systems: A review, Appl. Soft Comput., vol. 10, no. 1, pp. 135, 2010.
  7. Xia Wang, Intrusion Detection Techniques inWireless Ad Hoc Networks, Proceedings of the 30th Annual International Computer Software and Applications Conference (COMPSAC’06) 0-7695-2655-1/06 2006 IEEE
  8. Yan Wen, Jinjing Zhao, Huaimin Wang,A Novel Intrusion-Tolerant Approach for Internet Access, 978-0-7695-3151-9/08 2008 IEEE DOI 10.1109/ISIP.2008.28
  9. Manjiri V. Kotpalliwar, Rakhi Wajgi, Classification of Attacks Using Support Vector Machine (SVM) on KDDCUP99 IDS Database, 978-1-4799-1797-6/15 2015 IEEE DOI 10.1109/CSNT.2015.185
  10. Zhi-tang Li, Jie Lei, Li Wang, and Dong Li,A Data Mining Approach to Generating Network Attack Graph for Intrusion Prediction, Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007) 0-7695-2874-0/07 2007 IEEE
  11. Surat Srinoy, Student Member, IEEE ,Intrusion Detection Model Based On Particle Swarm Optimization and Support Vector Machine, 1-4244-0700-1/07/ 2007 IEEE
  12. Mostafa Doroudian, Narges Arastouie, Mohammad Talebi, Ali Reza Ghanbarian, Multilayered Database Intrusion Detection System for Detecting Malicious Behaviors in Big Data Transaction, ISBN: 978-1-4673-6988-6 2015 IEEE
  13. A. Markov, Extension of the limit theorems of probability theory to a sum of variables connected in a chain, Dynamic Probabilistic Systems, vol. 1, R. Howard. Hoboken, NJ, USA: Wiley, 1971 (Reprinted in Appendix B).
  14. L. E. Baum and J. A. Eagon, An inequality with applications to statistical estimation for probabilistic functions of Markov processes and to a model for ecology, Bull. Amer. Math. Soc., vol. 73, no. 3, p. 360, 1967.
  15. A. Arnes, F. Valeur, G. Vigna, and R. A. Kemmerer, Using Hidden markov models to evaluate the risks of intrusions: System architecture and model validation, Lect. Notes Comput. Sci., pp. 145164, 2006.
  16. D. Ariu, R. Tronci, and G. Giacinto, HMMPayl: An intrusion detection system based on hiddenMarkov models, Comput. Secur., vol. 30, no. 4, pp. 221241, 2011.
  17. S. S. Joshi and V. V. Phoha, Investigating hidden Markov models capabilities in anomaly detection, in Proc. ACM 43rd Annu. Southeast Reg. Conf., 2005, vol. 1, pp. 98103.
  18. J. Zhang, M. Zulkernine, and A. Haque, Random-forests-based network intrusion detection systems, IEEE Trans. Syst. Man Cybern. C: Appl. Rev., vol. 38, no. 5, pp. 649659, Sep. 2008.
  19. F. Gharibian and A. Ghorbani, Comparative study of supervised machine learning techniques for intrusion detection, in Proc. 5th Annu. Conf. Commun. Netw. Serv. Res., 2007, pp. 350358.
  20. J. H. Friedman, Multivariate adaptive regression splines, Anal. Statist., vol. 19, pp. 1141, 1991.
  21. S. Mukkamala, A. Sunga, and A. Abraham, Intrusion detection using an ensemble of intelligent paradigms, J. Netw. Comput. Appl., vol. 28, no. 2, pp. 167182, 2004.
  22. L. Bilge, D. Balzarotti, W. Robertson, E. Kirda, and C. Kruegel, Disclosure: Detecting botnet command and control servers through large-scale netflow analysis, in Proc. 28th Annu. Comput. Secur. Appl. Conf. (ACSAC12), Orlando, FL, USA, Dec. 37, 2012, pp. 129138.
  23. R. Agrawal, T. Imielinski, and A. Swami, Mining Association Rules between Sets of Items in Large Databases, Proc. ACM SIGMOD, vol. 22, no. 2, pp. 207-216, 1993.
  24. Y. Bouzida and S. Gombault, Eigen connections to Intrusion Detection, Security and Protection in Information Processing Systems, pp. 241-258, 2004.
  25. Raja Azrina Raja Othman, "Understanding the Various Types of Denial of Service Attack"
  26. Slvia Farraposo, Laurent Gallon, Philippe Owezarski,"Network Security and DoS Attacks"

International Journal of Scientific Research in Science and Technology

Downloads

Published

2017-07-22

Issue

Volume 3, Issue 6, July-August-2017

Section

Research Articles

License

Copyright (c) IJSRST

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Rashmi Chaudhari, Sonal Patil, " Intrusion Detection Using Secured and Efficient Data Mining, International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011, Volume 3, Issue 6, pp.93-101, July-August-2017.