A Novel Hierarchical Multi-classifier for Imbalanced Dataset in Network Intrusion Detection
Date Issued
2015
Date
2015
Author(s)
Zhang, Zhi-Jie
Abstract
Recently, under the popularity of mobile device and the driving of cloud computing, the network activities has grown remarkably. Thus, the Intrusion Detection Systems become very important. Compare to the regular connection, the attacks are relatively lesser in actual Internet traffic. Therefore, lots of supervisor’s intrusion detection systems, which are designed by the basis of statistical model are not easy to detect and classify those few but harmful attacking. In the paper, we propose an Intrusion Detection System which is based on the multi-classifier that can balance the numbers of data through hierarchical classifications. The different sensitivity of all various error cost and the numbers of data included in class are the basis of dividing. We take multi binary-classifier and single multiclass classifier to find every class from data in order. The benefit of the way is rich of flexibility and suitable for all kinds of popular classifcation algorithms. During intrusion detecting, it can less the classify errors which were caused by the variances in the numbers of all types of original training data set without modifying the distribution of original training data. It also less the average cost for intrusion detection data which are sensitive to error cost. The assessment of experimental method and result will be testified adopting KDD CUP 99 and the modified ND-KDD. In the ND-KDD, the four kinds of algorithms, which are hierarchical multi classifications can less 16% error rates and 13% average costs.
Subjects
Intrusion detection systems
imbalanced dataset
hierarchical classifier
Type
thesis
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