By Topic

Cascaded classifier approach based on Adaboost to increase detection rate of rare network attack categories

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
P. Natesan ; Department of CSE, Kongu engineering College, Erode Tamilnadu India ; P. Rajesh

Network intrusion detection often finds a difficulty in creating classifiers that could handle unequal distributed attack categories. Generally R2L and U2R attacks are very rare attacks and even in KDD Cup99 dataset, these attacks are only 2% of overall datasets. So, these result in model not able to efficiently learn the characteristics of rare categories and this will result in poor detection rates of rare attack categories like R2L and U2R attacks. We introduce a new approach called cascading classification model based on AdaBoost and Bayesian Network Classifier that can improve the detection rate of rare network attack categories. In this approach we train two classifiers with two different training sets. The KDD Cup99 dataset was splitted into two training sets where one contains full of non rare attacks datasets and other contains datasets of rare attack categories. This cascaded classifier approach increases the detection rates of both rare network attack categories and also it increase overall detection rate of an IDS model. The higher detection rates are due to the mitigation of the influence from the dominant categories if the rare attack categories are separated from the dataset.

Published in:

Recent Trends In Information Technology (ICRTIT), 2012 International Conference on

Date of Conference:

19-21 April 2012