Random Forest Algorithm in Intrusion Detection System : A Survey

Authors

  • Kritika Singh  Ambedkar Institute of Advanced Communication Technologies and Research, Department of Computer Science & Engineering, New Delhi, India
  • Bharti Nagpal  Ambedkar Institute of Advanced Communication Technologies and Research, Department of Computer Science & Engineering, New Delhi, India

Keywords:

Random Forest, Intrusion Detection System, NIDS, HIDS

Abstract

A randomized forest algorithm is based on the classification algorithm under supervision. In this algorithm, the forest is created randomly. The more the number of trees is present, the more accurate result they produced. It is important to note that decision-making using the gain or gain approach is not the same as creating a random forest. This paper presents a survey of Random Forest and other data mining techniques used in Intrusion Detection System.

References

  1. J. P. Anderson, Computer Security Threat Monitoring and Surveillance, Technical Report, James Anderson Report, Pennsylvania, (1980).
  2. D. Barbara and S. Jajodia, Applications of Data Mining in Computer Security. Norwell, MA: Kluwer, 2002.
  3. D. Anderson, T. Frivold, and A. Valdes, "Next-generation intrusion detection expert system (NIDES)-A summary," SRI Int., Menlo Park, CA, Tech. Rep. SRI-CSL-95-07, May 1995.
  4. D. Barbara, J. Couto, S. Jajodia, L. Popyack, and N. Wu, "ADAM: Detecting intrusions by data mining," in Proc. 2nd Annu. IEEE Workshop Inf. Assur. Secur., New York, Jun. 2001, pp. 11-16.
  5. E. Tombini, H. Debar, L. Me, and M. Ducasse, "A serial combination of anomaly and misuse IDSes applied to HTTP traffic," in Proc. 20th Annual Computer Security. Appl. Conf., Tucson, AZ, Dec. 2004, pp. 428-437.
  6. Manikandan R, Oviya P, and Hemalatha C, "A new data mining based network intrusion detection model," Journal of Computer Applications, vol.5, February 2012.
  7. E. Eskin, A. Arnold, M. Prerau, L. Portnoy, and S. Stolfo, "A geometric framework for unsupervised anomaly detection: Detecting intrusions in unlabeled data," in Applications of Data Mining in Computer Security. Norwell, MA: Kluwer, 2002.
  8. Ndumiyana, David. "Data mining techniques in intrusion detection: tightening network security." Unspecified (2013).
  9. Zhang, Jiong, Mohammad Zulkernine, and Anwar Haque. "Random-forests-based network intrusion detection systems." IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) 38.5 (2008): 649-659.
  10. Hasan, Md Al Mehedi,"Feature selection for intrusion detection using random forest." Journal of information security 7.03 (2016): 129.
  11. Johnson, Susan Rose, and Anurag Jain. "An Improved Intrusion Detection System using Random Forest and Random Projection." Probe 2: U2R.
  12. Farnaaz, Nabila, and M. A. Jabbar. "Random forest modeling for network intrusion detection system." Procedia Computer Science 89 (2016): 213-217.
  13. K. Leung and C. Leckie, "Unsupervised anomaly detection in network intrusion detection using clusters," in Proc. 28th Australasian CS Conf., Newcastle, Australia, Jan. 2005, vol. 38, pp. 333-342.
  14. R. Smith, A. Bivens, M. Embrechts, C. Palagiri, and B. Szymanski, "Clustering approaches for anomaly based intrusion detection," presented at the 1st Annu. Walter Lincoln Hawkins Graduate Res. Conf., New York, Oct. 2002.
  15. S. Bridges and R. Vaughn, "Fuzzy data mining and genetic algorithms applied to intrusion detection," in Proc. Nat. Inf. Syst. Secur. Conf. (NISSC), Baltimore, MD, Oct. 2000, pp. 13-31.
  16. A. Bivens, M. Embrechts, C. Palagiri, R. Smith, and B. Szymanski, "Network-based intrusion detection using neural networks," in Proc. Artif. Neural Netw. Eng., St. Louis, MO, Nov. 2002, vol. 12, pp. 527-535.
  17. M. Ramadas, S. Ostermann, andB. Tjaden, "Detecting anomalous network traffic with self-organizing maps," in Proc. Recent Adv. Intrusion Detect. (RAID), Pittsburgh, PA, Sep. 2003, Lecture Notes in Computer Science, vol. 2820, pp. 36-54.
  18. Q. Tran, H. Duan, and X. Li, "One-class support vector machine for anomaly network traffic detection," presented at the 2nd Netw. Res. Workshop 18th APAN, Cairns, Australia, Jul. 2004.
  19. Deepanshu B., (2014). Random Forest Tutorial. Retrieved from https://www.listendata.com/2014/11/random-forest-with-r.html
  20. Saimadhu P., (2017, May 22). How Random Forest Algorithm works in Machine Learning. Retrieved from http://dataaspirant.com/2017/05/22/random-forest-algorithm-machine-learing/
  21. Mohammadreza Ektefa, Sara Memar, Fatimah Sidi, Lilly Suriani Affendey,"Intrusion Detection Using Data Mining Techniques," Proceesings of IEEE International Conference on Information Retrieval & Knowledge Management , Exploring Invisible World, CAMP'10,2010,pp.200-203.
  22. Hai Nguyen, Katrin Franke and Slobodan Petrovi'c, "Improving Effectiveness of Intrusion Detection by Correlation Feature Selection," International Conference on Availability, Reliability and Security, pp. 17-24, IEEE 2010.

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2018-06-30

Issue

Volume 3, Issue 5, May-June-2018

Section

Research Articles

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

How to Cite

[1]
Kritika Singh, Bharti Nagpal, " Random Forest Algorithm in Intrusion Detection System : A Survey, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 5, pp.673-676, May-June-2018.