Intrusion Detection System Based on Modified K-Means Clustering Algorithm

Authors

  • Sarma N GIMT, Guwahati-17, Assam
  • Roy S GIMT,Guwahati
  • Nath J GIMT,Guwahati
  • Sarma A GIMT,Guwahati
  • Bora H GIMT,Guwahati

Keywords:

IDS, Data mining, KDD Cup, Clustering, Fuzzy, False Positive

Abstract

Due to the growth of Information Systems, different types of electronic attacks are happening day by day. This leads to the security breach rising every day Therefore it is of utmost important to protect highly sensitive and private information by securing the data. An intrusion detection system (IDS) monitors network or system activities and for nasty activities produces reports to a management. It monitors network traffic and its suspicious behaviour against security. Different types of intrusion detection methodologies are available, but all the current IDS are not perfect. Now a day’s Data mining concepts are used in the area of research in intrusion detection implementation. This paper tries to forward an idea of modifying the traditional K- means algorithm using fuzzy concept to prepare a model of intrusion detection system. The experiments have been done on the KDD Cup 99 dataset.

References

Shakil Ghalib, Kapil Dewan “Review on Intrusion Detection Approaches with Machine Learning” ©2015,IJARCSSE All Rights Reserved.

Hitesh Chandra Mahawari and Mahesh Pawar , "A Study of Various Methods to find K for K-Means Clustering", International Journal of Computer Sciences and Engineering, Volume-04, Issue-03, Page No (45-47), Mar -2016

. Vidit pathak, Dr. Ananthanarayana V.S. ”A Novel Multi-Threaded K-Means Clustering Approach for Intrsion Detection ”978-1-4673-2008-5/12/$31.00_2012 IEEE.

.Li Tian, Wang Jianwen”Research on Network Intrusion Detection System Based on Improved k-means Clustering Algorithm”978-0-7695-3930-0/09 $26.00 _ 2009 IEEE.

Kapil Wankhande, Sadia Patka”An Overview of Intrusion Detection Based on Data Mining Techniques”978-0-7695-4958-3/13 $26.00©2013 IEEE DOI 10.1109/CSNT.2013.134.

Pasquale Foggia, Gennaro Percannella,Carlo Sansone andmario vento,”A Graph-Based Clustering Method and It’s Applications”F.Male et al. (Eds.):BVAI 2007, LNCS 4729, pp.277-287,2007.©Springer.Verlag Berlin Heidelberg 2007.

Zhou mingqiang, Huang Hui, Wang Qian,”A Graph-based Clustering Algorithm for Anomaly Intrusion Detection ”978-1-4673-0242-5/12/$31.00©2012 IEEE.

Hachi fatma, Limam mohamed, ”A two-stage technique to improve intrusion detection system based on data mining algorithms”978-1-4673-5814-9/13/$31.00©2013 IEEE.

Enakshmi Nandi and Debabrata Sarddar, "A Modified MapReduce-K-Means Clustering Based Load Distribution Method for Wireless Sensor Network in Mobile Cloud computing", International Journal of Computer Sciences and Engineering, Volume-04, Issue-08, Page No (107-110), Aug -2016,

Nilam Kolhe, Harshada Kulkarni, Ishita Kedia and Shivani Gaikwad, "Comparative Analysis of Cluster based Boosting", International Journal of Computer Sciences and Engineering, Volume-03, Issue-10, Page No (60-70), Oct -2015

LIU Hai-tao, WEI Ru-xiang and JIANG Guo-ping” Similarity measurement for data with high-dimensional and mixed feature values through fuzzy clustering” in Proceedings of IEEE conference,2012 ,pp-617-621

KddCup-99 Team, The third international knowledge discovery and data mining tools competition, Available from http://kdd.ics.uci.edu/databases/ kddcup99.html, 2002

Dwipen Laskar, Nipjyoti Sarma, A Review On Intrusion Detection SystemUsing Data Mining Techniques,2013, MIT publications, Moradabad , India

Li Tian, Wang Jianwen, 2009 International Forum on Computer Science-Technology and Applications, 978-0-7695-3930-0/09 $26.00 © 2009 IEEE, DOI 10.1109/IFCSTA.2009.25.

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Published

2025-11-11

How to Cite

[1]
N. Sarma, S. Roy, J. Nath, A. Sarma, and H. Bora, “Intrusion Detection System Based on Modified K-Means Clustering Algorithm”, Int. J. Comp. Sci. Eng., vol. 4, no. 7, pp. 34–37, Nov. 2025.

Issue

Vol. 4 No. 7 (2016): IJCSE Special Issue December Edition

Section

Research Article

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