Network Log Clustering Using K-Means Algorithm

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 150)

Abstract

Network attacks are a serious issue in today’s network environment. The different network security alert system analyse network log files to detect these attacks. Clustering is useful for wide variety of real time applications dealing with large amount of data. Clustering divides the raw data into clusters. These clusters contain data points which have similarity between themselves and dissimilarity with other cluster data points. If these clusters are given to these security alert systems, they will take less time in analysis as the data will be grouped according to the criteria the security system needs. This can be done by using k means clustering algorithm. In this first number of clusters are selected and then centroids are initialized. Then data points are assigned to the cluster with nearest centroid and mean of the centroid is calculated. This step is repeated till no data points are left. The objective is to cluster the network data log so as to make it easier for different security alert systems to analyse the data and detect network attacks.

Keywords

Data clustering K means algorithm Centroid Data points Network attacks Network traffic log 

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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.Department of Computer EngineeringSinhgad Institute of TechnologyLonavalaIndia

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