A new data mining based hybrid network intrusion detection model

Proceedings - 2012 International Conference on Data Science and Engineering, ICDSE 2012

Volumn , Issue , 2012, Pages 52-57

  Barot, Virendra ^a   Toshniwal, Durga ^a  

^a INDIAN INSTITUTE OF TECHNOLOGY ROORKEE   (India)

Author keywords

decision table majority; hybrid approach; network intrusion detection system

Indexed keywords

BAYESIAN APPROACHES; CLASSIFICATION MODELS; DETECTION RATES; FALSE POSITIVE RATES; HYBRID APPROACH; HYBRID MODEL; HYBRID NETWORK; INTRUSION DETECTION SYSTEMS; MALICIOUS ACTIVITIES; NAIVE BAYES; NETWORK INTRUSION DETECTION SYSTEMS; NETWORK TRAFFIC ANALYSIS; RULE BASED;

BAYESIAN NETWORKS; CLASSIFIERS; COMPUTER CRIME; DECISION TABLES; WEBSITES;

INTRUSION DETECTION;

EID: 84867354576     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDSE.2012.6282310     Document Type: Conference Paper

References (17)

1. rd Int. SIAM Conf. on Data Mining, pp. 1-13, 2003.
2. Wenke Lee, Salvotore J. Stolfo and Kui W. Mok, "A Data Mining Framework for Building Intrusion Detection Model", Proceedings of the IEEE Symposium on Security and Privacy, pages 120-132, 1999.
3. E. Eskin, A. Arnold, M. Preau, L.Portnoy, and S. Stolfo, "A Geometric Framework for Unsupervised Anomaly Detection: Detecting Intrusions in Unlabeled Data" in Applications of Data Mining in Computer Security, Kluwer Academic Publishers, 2002.
4. P. Srinivasulu, D. Nagaraju, P. Ramesh Kumar, and K. Nageshwara Rao, "Classifying the network intrusion attacks using data mining Classification methods", International Journal of computer science and network security (IJCSNS),vol. 9, June 2009.
5. Ron Kohavi, "The power of decision Tables" In 8th European conference on Machine learning, pp.174-189, 1995.
6. th Int. Florida Artificial Intelligence Research Society Conference(FLAIRS), Orlando, FL, pp. 235-239, 1999.
7. J. Han and M. Kamber. Data Mining: Concepts and Techniques. Morgan Kaugmann Publishers, San Francisco, 2009.
8. Dewan M.D. Ferid, Nouria Harbi, "Combining Naïve Bayes and Decision Tree for Adaptive Intrusion detection" International Journal of Network Security and application(IJNSA),vol 2, pp. 189-196, April 2010.
9. Joseph Derrick,Richard W. Tibbs, Larry Lee Reynolds "Investigating new approaches to data collection,management and analysis for network intrusion detection". In Proceeding of the 45th annual southesast regional conference, 2007. DOI = http://dl.acm.org/citation.cfm?doid=1233341.1233392
10. M.Panda, M. Patra, "Ensemble rule based classifiers for detecting network intrusion detection", in Int. Conference on Advances in Recent Technology in Communication and Computing, pp 19-22,2009.
11. C.H. Tsang, S. Kwong and H. Wang, "Genetic-fuzzy rule mining approach and evaluation of feature selection techniques for anomaly intrusion detection," Pattern Recognition, Vol. 40, Issue 9, pp. 2373-2391, 2007.
12. C. F. Tsai, and C.Y Lin, "A triangle area-based nearest neighbors approach to intrusion detection," Pattern Recognition, Vol. 43, pp. 222-229 , 2010.
13. B.A. Nahla, B. Salem, and E. Zied, "Naive bayes vs decision trees in intrusion detection systems", In Proceeding of the ACM Symposium on Applied Computing, Nicosia, Cyprus, pp. 420-424, 2004.
14. Hongjie Liu, Boqin feng, jianjie weng, "An Effective Data Classification Algorithm Based on the Decision Table", in Seventh IEEE/ACIS International Conference on Computer and Information Science, 2008.
15. Wei Li and QingXia Li,"Using Naive Bayes with AdaBoost to Enhance Network Anomaly Intrusion Detection", in Third International Conference on Intelligent Networks and Intelligent Systems,pp. 486-489, 2010.
16. KDD(1999). Available at http://kdd.ics.uci.edu/databases/kddcup99/ kddcup99.html
17. http://weka.wikispaces.com/Eclipse+3.4.x+(weka-src.jar)