Manuscript Number : CSEIT1831141
Implementation of Improved K-Mean Algorithm for Intrusion Detection System to Improve the Detection Rate
Authors(2) :-Susheel Kumar Tiwari, Dr. Manish Shrivastava In Data mining there are lots of methods are used to detect the outlier by making the clusters of data and then detect the outlier from them. In general Clustering method plays a very important role in data mining. Clustering means grouping the similar data objects together based on the characteristic they possess. An improved K-means clustering algorithm is put forward on basis of the split-merge method for the purpose of remedying defects both in determination of value in K and in selection of initial cluster centre of traditional K-means clustering. At first , the concept of independence degree of date was incorporated into the experimental date subset construction theory , using independence degree to evaluate the importance of nature. Next ,the database is merged into several classes in respect of density of date points ,the combination of the minimum spanning tree algorithm and traditional K-means clustering algorithm is conducive to the achievement of splitting .Eventually ,the KDD Cup99 database is applied to conduct simulation experiment on the application of the improved algorithm in intrusion detection .The results indicate that the improved algorithm prevails over traditional K-means algorithm in detection rate and false alarm rate
Susheel Kumar Tiwari Intrusion Detection System, K-Mean, Clustering Publication Details Published in : Volume 3 | Issue 1 | January-February 2018 Article Preview
PhD Research Scholar, Mewar University, Chittorgarh, Rajasthan, India
Dr. Manish Shrivastava
Professor & Head (CSE) L.N.C.T Bhopal, Affiliated to R.G.P.V Bhopal, Madhya Pradesh, India
Date of Publication : 2018-02-28
License: This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 649-654
Manuscript Number : CSEIT1831141
Publisher : Technoscience Academy