Paper Info
Title
A cascaded classifier approach for improving detection rates on rare attack categories in network intrusion detection
Abstract
Network intrusion detection research work that employed KDDCup 99 dataset often encounter challenges in creating classifiers that could handle unequal distributed attack categories. The accuracy of a classification model could be jeopardized if the distribution of attack categories in a training dataset is heavily imbalanced where the rare categories are less than 2% of the total population. In such cases, the model could not efficiently learn the characteristics of rare categories and this will result in poor detection rates. In this research, we introduce an efficient and effective approach in dealing with the unequal distribution of attack categories. Our approach relies on the training of cascaded classifiers using a dichotomized training dataset in each cascading stage. The training dataset is dichotomized based on the rare and non-rare attack categories. The empirical findings support our arguments that training cascaded classifiers using the dichotomized dataset provides higher detection rates on the rare categories as well as comparably higher detection rates for the non-rare attack categories as compared to the findings reported in other research works. 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.
Year
DOI
Venue
2012
10.1007/s10489-010-0263-y
Applied Intelligence
Keywords
Field
DocType
Network intrusion detection,Cascaded classifiers,Imbalanced dataset
Data mining,Population,Network intrusion detection,Pattern recognition,Computer science,Artificial intelligence,Classifier (linguistics),Machine learning
Journal
Volume
Issue
ISSN
36
2
0924-669X
Citations 
PageRank 
References 
29
0.83
31
Authors
3
Name
Order
Citations
PageRank
Kok-Chin Khor1363.05
Choo-Yee Ting29013.19
Somnuk Phon-Amnuaisuk319425.89