— 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.
— K-Means, C-Means, KDD Cup99, GureKDD, NSLKDD.
The authors are with the National Institute of Technology, Rourkela, Odisha, 769008 India (e-mail: firstname.lastname@example.org, email@example.com).
Cite: Santosh Kumar Sahu and Sanjay Kumar Jena, " A Study of K-Means and C-Means Clustering Algorithms for Intrusion Detection Product Development," International Journal of Innovation, Management and Technology vol. 5, no. 3, pp. 207-213, 2014.