International Journal of Computer
Trends and Technology

Research Article | Open Access | Download PDF

Volume 3 | Issue 6 | Year 2012 | Article Id. IJCTT-V3I6P102 | DOI : https://doi.org/10.14445/22312803/IJCTT-V3I6P102

Reducing Network Intrusion Detection using Association rule and Classification algorithms

K.Keerthi, P.Sreenivas

Citation :

K.Keerthi, P.Sreenivas, "Reducing Network Intrusion Detection using Association rule and Classification algorithms," International Journal of Computer Trends and Technology (IJCTT), vol. 3, no. 6, pp. 752-756, 2012. Crossref, https://doi.org/10.14445/22312803/IJCTT-V3I6P102

Abstract

IDS (Intrusion Detection system) is an active and driving defense technology. This project mainly focuses on intrusion detection based on data mining. Data mining is to identify valid, novel, potentially useful, and ultimately understandable patterns in massive data. This project presents an approach to detect intrusion based on data mining frame work. Intrusion Detection System (IDS) is a popular tool to secure network. Applying data mining has increased the quality of intrusion detection neither as anomaly detection or misused detection from large scale network traffic transaction. Association rules is a popular technique to produce a quality misused detection. However, the weaknesses of association rules is the fact that it often produced with thousands rules which reduce the performance of IDS.

Keywords

Association Rules, Association Rule Mining, Ontology, correlation measures, user constraints.

References

[1] Quinlan J R, ”Simplifying Decision Tree,” Internet Journal of Man-Machine Studies,1987,27,pp.221-234
[2] Yang Xue-bing,Zhang Jun, ”Decision Tree Algorithm and its core technology,” Computer Technology and Development,2007, 17(1),pp.43-45
[3] Qu Kai-she,Wen Cheng-li,Wang Jun-hong, ”An improved algorithm of ID3 algorithm,” Computer Engineering and Applications, 2003,(25),pp.104-107
[4] Mao Cong-li Yi Bo, ”The most simple decision tree generation algorithm based on decision-making degree of coordination ,“ Computer Engineering and Design,2008,29(5),pp.1250-1252
[5] Huang Ai-hui, ”Improvement and application of decision tree C4.5 algorithm ,“ Science Technology and Engineering,2009, (1),pp.34-37
[6] J. Gehrke, R. Ramakrishnan, and V. Ganti, "Rainforest, a framework for fast decision tree construction of large datasets", in Springer Netherlands-Data mining and knowledge discovery vol.4. Issue(2-3) July 2000.
[7] M. Kantardzic “Data Mining. Concepts, Models, Methods and Algoritms”. John Wiley and Sons Inc, 2003.
[8] Xu.M.Wang, J. and Chen.T. “Improved decision tree algorithm: ID3+” Intelligent Computing in Signal Processing and Pattern Recognition, Vol.345, pp.141-149, 2006.
[9] Quinlan, J. R. “C4.5: Programs for Machine Learning” Morgan Kaufmann, San Mateo, CA 1993.
[10] Lewis, R.J. “An Introduction to Classification and Regression Tree (CART) Analysis” Annual Meeting of the Society for Academic Emergency Medicine, Francisco 2000.
[11] Ruoming Jin, Ge Yang and Gagan Agrawal, “Shared memory parallelization of Data mining algorithms: Techniques, Programming interface and Performance”, IEEE Transactions on Knowledge & data engineering, 2005.
[12] Song Xudong, Cheng Xiaolan “Decision tree Algorithm based on Sampling” IFIP International conference on Netwok and Parallel Computing-Workshops 2007.