Please use this identifier to cite or link to this item:
http://hdl.handle.net/2080/2150
Title: | A Study of K-Means and C-Means Clustering Algorithms for Intrusion Detection Product Development |
Authors: | Sahu, S K Jena, S K |
Keywords: | K-Means C-Mean KDD Cup99 GureKDD NSLKDD |
Issue Date: | Jun-2014 |
Citation: | 2nd Journal Conference on Innovation, Management and Technology (JCIMT 2014 2nd),16-17th June,2014 Hong Kong |
Abstract: | The increase in Internet and Internet based application, the business premises have now spread throughout the world. Due to the extreme competitions among the business, one tries to demolish other. Hence, secure product design techniques should be adopted. To protect the applications from intruder, intrusion detection system becomes utmost requirement for every organization. In intrusion detection models enormous quantity of training data is required. As a result, sophisticated algorithms and high computational resources are required. In Intrusion Detection System, to separate normal activities from abnormal activities clustering algorithms are used. To select an efficient clustering algorithm is a challenging task. In this paper, a comparison has been made between K-Means and C-Means clustering on intrusion datasets. The simulation contains all proximity measures of K-Means and C-Means clustering techniques. The accuracy of these clustering algorithms is compared using the confusion matrix.The result shows that K-Means provides better clustering accuracy in comparison with C-Means. Therefore, to design intelligent intrusion detection product K-Means is a better option. |
Description: | Copyright belongs to the Proceeding of Publisher. |
URI: | http://hdl.handle.net/2080/2150 |
Appears in Collections: | Conference Papers |
Files in This Item:
File | Description | Size | Format | |
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IJIMT_Camera_Ready -CM308.pdf | 458.65 kB | Adobe PDF | View/Open |
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