Anomaly Intrusion Detection System using Random Forests and k-Nearest Neighbor

  IJPTT-book-cover
 
International Journal of P2P Network Trends and Technology (IJPTT)          
 
© 2013 by IJPTT Journal
Volume-3 Issue-1                           
Year of Publication : 2013
Authors : Phyu Thi Htun, Kyaw Thet Khaing

MLA

Phyu Thi Htun, Kyaw Thet Khaing"Anomaly Intrusion Detection System using Random Forests and k-Nearest Neighbor". International Journal of P2P Network Trends and Technology (IJPTT), V3(1):39-43  Jan - Feb 2013, ISSN:2249-2615, www.ijpttjournal.org. Published by Seventh Sense Research Group.

Abstract

This paper proposed a new approach to design the anomaly intrusion detection system using not only misuse but also anomaly intrusion detection for both training and detection of normal or attacks respectively. The utilized method is the combination of Machine Learning and pattern recognition method for Anomaly Intrusion Detection System (AIDS).

References

[1] W. Lee and S. J. Stolfo, “Data Mining Approaches for Intrusion Detection”, the 7th USENIX Security Symposium, San Antonio, TX, January 1998.
[2] K.T.Khaing and T.T.Naing, “Enhanced Feature Ranking and Selection using Recurisive Featue Elemination and k-Nearest Neighbor Algorithms in SVM for IDS”, Internaiton Journal of Network and Mobile Technology(IJNMT), No.1, Vol 1. 2010.
[3] M. Bahrololum, E. Salahi and M. Khaleghi, "Anomaly Intrusion Detection Design using Hybrid of Unsupervised and Supervised Neural Network", International Journal of Computer Network & Communications(IJCNC), Vol.1, No.2, July 2009.
[4] L. Breiman, “Random Forests”, Machine Learning 45(1):5–32, 2001.
[5] V. Marinova-Boncheva, "A Short Survey of Intrusion Detection System" , 2007.
[6] Tamas Abraham, “IDDM: Intrusion Detection Using Data Mining Techniques”, DSTO Electronics and Surveillance Research Laboratory, Salisbury, Australia, May 2001.
[7] M. Mahoney and P. Chan, “An Analysis of the 1999 DARPA/Lincoln Laboratory Evaluation Data for Network Anomaly Detection”, Proceeding of Recent Advances in Intrusion Detection (RAID)-2003, Pittsburgh, USA, September 2003.
[8] KDD’99 datasets, The UCI KDD Archive, http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html , Irvine, CA, USA, 1999.
[9] KDD Cup 1999. Available on: http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html, December 2009.
[10] Lan Guo, Yan Ma, Bojan Cukic, and Harshinder Singh, “Robust Prediction of Fault-Proneness by Random Forests”, Proceedings of the 15th International Symposium on Software Reliability Engineering (ISSRE'04), pp. 417-428, Brittany, France, November 2004.

Keywords

AIDS, Random Forest, k-Nearest Neighbour, unknown attacks