An Analysis of K-means Algorithm Based Network Intrusion Detection System

Author:

Aung Yi Yi,Min Myat Myat

Publisher

ASTES Journal

Subject

Management of Technology and Innovation,Physics and Astronomy (miscellaneous),Engineering (miscellaneous)

Reference17 articles.

1. Aung Yi Yi and Myat Myat Min, "An Analysis of Random Forest Algorithm Based Network Intrusion Detection System", Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNDP), 2017 IEEE/ACIS 18 th International Conference, 2017.

2. K.Q., S. C. Wang and C. W. Liu, "A Hybrid Intrusion Detection System of Cluster-based Wireless Sensor Networks", Proceedings of the International MultiConference of Engineers and Computer Scientists 2009 Vol I, IMECS 2009, March 18-20, 2009, Hong Kong.

3. Revathi and Dr. A. Malathi, "Detecting User-To-Root (U2R) Attacks Based on Various Machine Learning Techniques", International Journal of Advanced Research in Computer and Communication Engineering, Vol. 3, Issue 4, ISSN (Online): 2278-1021, ISSN (Print): 2319-5940, April 2014.

4. Revathi and Dr. A. Malathi, "Detecting Denial of Service Attack Using Principal Component Analysis with Random Forest Classifier", International Journal of Computer Science & Engineering Technology (IJCSET), Vol.5, No. 03, ISSN: 2229-3345, March 2014.

5. Jayshri R. Patel, "Performance Evaluation of Decision Tree Classifiers for Ranked Features of Intrusion Detection", Journal of Information, Knowledge and Research in Information Technology, Vol. 02, Issue -02, ISSN: 0975-6698, Nov 12 to Oct 13.

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5. Performance analysis of an intrusion detection system for wireless adhoc network in the detection of DOS attack using K-means cluster and K-NN algorithm;AIP Conference Proceedings;2023

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