Analyzing Intrusion Detection Using Machine Learning Adaboost Algorithm : An Observations Study

  ijett-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
© 2013 by IJETT Journal
Volume-4 Issue-6                      
Year of Publication : 2013
Authors : Ms.S.S.Kazi , Dr.P.R.Deshmukh


Ms.S.S.Kazi , Dr.P.R.Deshmukh. "Analyzing Intrusion Detection Using Machine Learning Adaboost Algorithm : An Observations Study". International Journal of Engineering Trends and Technology (IJETT). V4(6):2302-2304 Jun 2013. ISSN:2231-5381. published by seventh sense research group.


The current approaches for intrusion detection have some problems that adversely affect the effectiveness of the Intrusion Detection System. Current approaches often suffer from relatively high false - alarm rates. As most network behaviors are normal, resources are wasted on checking a large number of alarms that turn out to be false. Secondly their computational complexities are oppressively high. The adaboost algorithm gives bett er results for intrusion detection in this respect. The paper here mainly has its focus on these results.


[1] “ Network based Intrusion Detection Using A daboosl Algorithm” Proceedings of the 2005 IEEE/WIC/ACM International Conference on Web Intelligence (WI’05) ,Wei Hu and Weiming Hu
[2] ”An Enhanced Support Vector Machine Model for Intrusion Detection” JingTao Yao, Songlun Zhao, and Lisa Fan
[3] Z. Zhang and H. Shen: Online training of svms for real time intrusion d etection based on improved text categorization model. Computer Communications. February 2005
[4] C. Elkan. Results of the kdd99 classifier learning contest. SIGKDD Explorations, 1(2):63 - 64, 2000.
[5] D. Denning. An intrusion - detection model. IEEE Transactions on Software Engineering. 13(2):222 - 232, February 1987.
[6] Y. Freund and R. E. Schapire . A decision - theoretic generalization of on - line learning and an application to boosting. Computer and System Sciences, August 1997
[7] P. Hong, Zhang, and T. Wu “Intrusion detection method based on rough set and svm” algorithm. In Proceedings of Internati onal Conference on Communications, Circuits and Systems, volume 2, pages 1127 - 1 130, June 2004
[8] H. G. Kayacik, A. Zincir - Heywood, and M. Heywood. On the capability of an som based intrusion detection system. In Proceedings of the International Joint Con ference on Neural Networks, volume 3, pages 1808 - 1813, July 2003.
[9] W.Lee, S.J.Stolfo. A framework for constructing features and models for intrusion detection systems. ACM Transactions on Information and System Security, 3(4):227 - 261, November 2000.

Detection Rate, False Alarm Rate, False Positive Rate, False Negative Rate