The week's pick
Reseach Article
Data Understanding Analysis for Analytical Mining IDS
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 75 - Number 7 |
| Year of Publication: 2013 |
| Authors: Anurag Bhardwaj, Divydeep Agarwal |
10.5120/13121-0471
|
Anurag Bhardwaj, Divydeep Agarwal . Data Understanding Analysis for Analytical Mining IDS. International Journal of Computer Applications. 75, 7 ( August 2013), 10-13. DOI=10.5120/13121-0471
Abstract
With the ephemeral time every information stands a greater risk of being exposed than ever before. System's security is endangered in a blink and intrusion takes place [8]. Keeping this in mind, the effectiveness of various data mining approaches are discussed. Some methods involved in classification and clustering are stated. Analysis of SVM classifier and K-means clustering is also presented. Intrusion Detection System (IDS) maintains the integrity of the system, monitors network traffic detecting potential hostile activities [6]. A case study using Snort has been done. The key idea is to study various data mining techniques and how they can be applied to IDS to maximise the effectiveness in identifying attacks, and henceforth adding to the creation of a more secured system.
References
- Kaushik Sapna and Deshmukh P. R. , Comparison of approaches to implement intrusion detection system, International Journal of Computer Science and Communication, vol. 2, no. 1, pp. 45-48, Jan-Jun 2011.
- Chang-Tien Lu, Arnold P. Boedihardjo, Prajwal Manalwar, Exploiting efficient data mining techniques to enhance intrusion detection systems, pp. 512-517, IRI 2005.
- Chih-Fong Tsai, Yu-Feng Hsu, Chia-Ying Lin, Wei-Yang Lin, Intrusion detection by machine learning: A review, Expert Systems with Applications, 36. Jg. , Nr. 10, 2009.
- T. Abraham, IDDM: Intrusion Detection using Data Mining Techniques, DSTO-GD, 2008.
- Rakesh Agrawal and Ramakrishnan Srikant, Privacy-preserving data mining, In Proceedings of the 2000 ACM SIGMOD international conference on Management of data (SIGMOD '00), ACM, New York, NY, USA, 439-450, 2000.
- Min Qin and Kai Hwang, Anomaly Intrusion Detection by Internet Data mining of Traffic Episodes, ACM, TISSec, March 1, 2004.
- Alok Ranjan, Dr. Ravindra S. Hegadi, Prasanna Kumara, Emerging Trends in Data Mining for Intrusion Detection, International Journal of Advanced Research in Computer Science, vol. 3, no. 2, March-April 2012.
- Bhavani Thuraisingham, Latifur Khan, Mohammad M. Masud, Kevin W. Hamlen, Data Mining for Security Applications, IEEE/IFIP International Conference on Embedded and Ubiquitous Computing, 2008.
- Han Jiawei and Kamber Micheline, Data Mining: Concepts and Techniques, 2nd edition, San Francisco, Morgan Kaufmann Publishers, 2006.
- Li Bo, Jiang Dong-Dong, The Research of Intrusion Detection Model Based on Clustering Analysis, IEEE International Conference on Computer and Communications Security, 2009.
- A. K Maheshwari, Association Rule in Data Mining for Large Transactional Database, IJMIE, vol. 2, issue 2, pp. 358-380, March 2012.
- Dr. Sugumar Rajendran, Dr. Rengarajan Alwar, Dr. Saravanakumar Selvaraj, Determining the Existence of Quantitative Association Rule Hiding in Privacy Preserving Data Mining, IJARCCE, vol. 1, issue 2, April 2012.
- V. N. Vapnik, Statistical Learning Theory, Wiley, New York, 1998.
- C. M. Bishop, Neural Networks for Pattern Recognition, Oxford University Press, 1995.
- Manocha, S. and Girolami, M. A. , 2007, An empirical analysis of the probabilistic K-nearest neighbour classifier, Pattern Recognition Letters, vol. 28, 1818-1824.
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