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Application of k-Nearest Neighbour Classification Method for Intrusion Detection in Network Data
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 97 - Number 7 |
| Year of Publication: 2014 |
| Authors: S. Venkata Lakshmi, T. Edwin Prabakaran |
10.5120/17021-7306
|
S. Venkata Lakshmi, T. Edwin Prabakaran . Application of k-Nearest Neighbour Classification Method for Intrusion Detection in Network Data. International Journal of Computer Applications. 97, 7 ( July 2014), 34-37. DOI=10.5120/17021-7306
Abstract
In today's world, increasing number of networks connected to the Internet poses a great challenge on security issues. Many defensive mechanisms exist and one such higher level mechanism is network intrusion detection system. Intrusion detection system is a process of intelligently monitoring the events in an individual system or network, analysing them for signs of violation of security policy. Two major classifications of intrusion detection systems are misuse and anomaly intrusion detection systems. Misuse detection system refers to detection of intrusions that follow well defined intrusion patterns. Anomaly detection model refers to detection performed by detecting changes in the behaviour of the system. Many data mining techniques like k-Nearest Neighbour (kNN), Association Rule Mining etc. , have been applied to intrusion detection. This paper aims at application of kNN to a subset of records from the KDD Cup 1999 dataset for classification of connection records into normal or attacked data. The paper also applies kNN to the subset of records with the selected features proposed by Kok-Chin-Khor et al [5] to compare the classifications.
References
- Adebayo O. Adetunmbi*, Samuel O. Falaki, Olumide S. Adewale and Boniface K. Alese, "Network Intrusion Detection based on Rough Set and k-Nearest Neighbour", International Journal of Computing and ICT Research, Vol. 2, No. 1, pp. 60-66. ,2008, http://www. ijcir. org/volume1number2/article7. pdf.
- Ganapathy. S, Jaishankar. N, Yogesh. P and Kannan. A, "An Intelligent Intrusion Detection system using Outlier Detection and Multiclass SVM, Int. J. on Recent Trends in Engineering & Technology, Vol. 05,No. 01,166-169, 2011.
- Hossein M. Shirazi, "Anomaly Intrusion Detection using Information Theory,k-NN and KMC Algorithms", Australian Journal of Basic and Applied Sciences,3(3):2581-2597,2009.
- KDD CUP 1999 DATASET: http://kdd. ics. uci. edu/databases/kddcup99/
- Kok-Chin Khor, Choo-Yee Ting and Somnuk-Phon Amnuaisuk, "From Feature Selection to Building of Bayesian Classifiers: A Network Intrusion Detection Perspective", American Journal of Applied Sciences 6(11),1948-1959,2009.
- Lee. W, Stolfo. S. J, Mok. K. W, "Algorithms for Mining System Audit Data", of Proc. KDD,1999.
- Neveen I. Ghali , "Feature Selection for Effective Anomaly Based Intrusion Detection", International Journal of Computer Science and Network Security, Vol. 9 No. 3 March 2009.
- Norbik Bashah, Idris Bharanidharan Shanmugam, Abdul Manan Ahmed, "Hybrid Intelligent Intrusion Detection System", World Academy of Science, Engineering and Technology, pp 23-26,2005.
- Stolfo. S. J, Hershkop. S, Wang. K, Nimeskern. O, and Hu. C. W, "Behaviour Profiling of Email", First NSF/NIJ,ISI,2003.
- Victor-Valeriu Patriciu, Liviu Rusu, Iustin Priescu, "Data mining approaches for Intrusion Detection in Email system Internet-Based",Military Technical Academy, 144-147,2003.
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