Analyzing Intrusion Detection Using Machine Learning Adaboost Algorithm : An Observations Study
|International Journal of Engineering Trends and Technology (IJETT)||
|© 2013 by IJETT Journal|
|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. www.ijettjournal.org. 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.
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Detection Rate, False Alarm Rate, False Positive Rate, False Negative Rate