The week's pick
Reseach Article
A Naive Gain Approach to Intrusion Detection Systems
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 70 - Number 23 |
| Year of Publication: 2013 |
| Authors: Sonal Porwal, Deepali Vora |
10.5120/12210-8319
|
Sonal Porwal, Deepali Vora . A Naive Gain Approach to Intrusion Detection Systems. International Journal of Computer Applications. 70, 23 ( May 2013), 35-39. DOI=10.5120/12210-8319
Abstract
Today the world is dependent upon so many advanced technologies and network systems, that their protection from those which intent to break the system with malicious attacks, or trying some unauthorized access with an intention of financial gain or simply trying to intrude the system has become essential. This leads to the need of Intrusion Detection Systems. Many algorithms have been suggested to implement this system, which requires building of a training model by using a training data set. In this paper,NSL KDD data set will be used to train the system using Naïve Bayes approach and then there is an attempt to improve its accuracy by proposing an algorithm based on feature selection. A concept of threshold is also introduced which works on the principle of C4. 5 algorithm. The proposed algorithm is applied on another data set that is supplied by the user which is also a part of NSL KDD. This paper discusses the proposed algorithm which is used to improve the performance of the classification system of the Naïve Bayes Classifier and reduce the number of false alarm rate to some extent.
References
- Ahmed Youssef and Ahmed Emam,"Network Intrusion Detection Using Data Mining and Network Behaviour Analysis", International Journal of Computer Science & Information Technology (IJCSIT) Vol 3, No 6, Dec 2011Network Behaviour Analysis", International Journal of Computer Science & Information Technology (IJCSIT) Vol 3, No 6, Dec 2011
- Stefan Axelsson,"Intrusion Detection Systems:A Survey and Taxonomy", Department of Computer Engineering,Chalmers University of Technology,March2000
- Jiawei Han and MichelineKamber,"Data Mining: Concepts and Techniques", Simon Fraser Universitty,3rdedition, July 2011.
- Devi Prasad Bhukyaand S. Ramachandram," Decision Tree Induction: An Approach for Data Classification Using AVL-Tree",International Journal of Computer and Electrical Engineering, Vol. 2, No. 4, August, 2010.
- Mrutyunjaya Panda1 and ManasRanjan Patra2,"Network Intrusion Detection UsingNaïve Bayes",IJCSNS International Journal of Computer Science and Network Security, VOL. 7 No. 12, December 2007
- Panda, M. ; Patra, M. R. , "A Comparative Study of Data Mining Algorithms for Network Intrusion Detection," Emerging Trends in Engineering and Technology, 2008. ICETET '08. IEEE, vol. , no. , pp. 504,507, 16-18 July 2008
- K KBharti,"Intrusion detection using clustering",IJCCT,Vol. 1 for International Conference [ACCTA-2010], August 2010.
- S. VijayaPeterraj,"Study of Data Mining Techniques in Intrusion Detection",Fast Processing Peer Reviewed International Journals,2012.
- George J. Klir and Bo Yuan,"Fuzzy Sets and Fuzzy Logic:Theory and Applications". Prentice Hall,1995.
- John E. Dickerson," Fuzzy Network Profiling for Intrusion Detection", NAFIPS,pp. 301-306,July 2000,IEEE Explore.
- Kruegel, C. ; Mutz, D. ; Robertson, W. ; Valeur, F. , "Bayesian event classification for intrusion detection," Computer Security Applications Conference, 2003. Proceedings. 19th Annual, vol. , no. , pp. 14, 23, 8-12 Dec. 2003, IEEEExplore.
- Juan Wang, Qiren Yang," An intrusion detection algorithm based on decision tree technology", Asia-Pacific Conference on Information Processing, 2009, IEEE
Index Terms
Keywords