Information security is becoming a more important issue for modern computer generation, progressively. Intrusion Detection System (IDS) as the main security defensive technique and is widely used against many category of attacks. Intrusion Detection Systems are used to detect various kinds of attack in very large datasets. Data Mining (DM) and Machine Learning (ML) techniques are powerful enough and proved useful in the Network Intrusion Detection research area. In Recent years, many ML methods have also been introduced by researchers, to obtain high accuracy and good detection rate. A potential drawback of all those methods is how to classify different intrusion attacks effectively. Looking at such inadequacies, the RapidMiner tool is tested for the few ML techniques in this work. As most of the research works using tools like MATLAB, WEKA etc. the purpose of this work is to test and evaluate the ML techniques on RapidMiner. This paper presents a performance comparison of three ML techniques including: K-NN, Decision tree, Naïve Bayes using RapidMiner tool. This paper will provide an insight for the future research. The techniques were tested based on Detection rate and False Alarm rate. The result analysis and evaluation obtained by applying these approaches to the KDD CUP`99 data set.

Intrusion Detection System (IDS), Data Mining, Classification, Data Science, Machine Learning, RapidMiner, Security, KDD CUP’99

[1] Hua TANG, Zhuolin CAO “Machine Learning-based Intrusion Detection Algorithms” Journal of Computational Information Systems5:6(2009) 1825-1831 Available at http://www.JofCI.org
[2] YU-XIN MENG “The Practice on Using Machine Learning For Network Anomaly Intrusion Detection” 2011 IEEE
[3] Chi Cheng, Wee Peng Tay and Guang-Bin Huang “Extreme Learning Machines for Intrusion Detection” - WCCI 2012 IEEE World Congress on Computational Intelligence June, 10-15, 2012 - Brisbane, Australia
[4] Naeem Seliya , Taghi M. Khoshgoftaar “Active Learning with Neural Networks for Intrusion Detection” IEEE IRI 2010, August 4-6, 2010, Las Vegas, Nevada, USA 978-1-4244-8099-9/10/2010 IEEE
[5] Kamarularifin Abd Jalill, Mohamad Noorman Masrek “Comparison of Machine Learning Algorithms Performance in Detecting Network Intrusion” 201O International Conference on Networking and Information Technology 978-1-4244-7578-0, 2010 IEEE
[6] Jingbo Yuan , Haixiao Li, Shunli Ding , Limin Cao “Intrusion Detection Model based on Improved Support Vector Machine” Third International Symposium on Intelligent Information Technology and Security Informatics 978-0-7695-4020-7/10, 2010 IEEE
[7] Mohammadreza Ektefa, Sara Memar, Fatimah Sidi, Lilly Suriani Affendey “Intrusion Detection Using Data Mining Techniques” IEEE 2010.
[8] Anand Motwani, Vaibhav Patel, Anita Yadav, “Optimal Sampling for Class Balancing with Machine Learning Technique for Intrusion Detection System”, International Journal of Electrical, Electronics and Computer Engineering 4(2): 47-51(2015)
[9] Mahbod Tavallaee, Ebrahim Bagheri, Wei Lu, and Ali A. Ghorbani “A Detailed Analysis of the KDD CUP 99 Data Set” Proceedings of the 2009 IEEE Symposium on Computational Intelligence in Security and Defense Applications (CISDA 2009).
[10] Stuti Nathaniel, Anand Motwani, Arpit saxena, "Cloud based Predictive Model for Detection of ‘Chronic Kidney Disease’ Risk", International Journal of Computer Sciences and Engineering, Vol.6, Issue.4, pp.185-188, 2018
[11] http://www.rapidminer.com/