Performance Comparison of Machine Learning Techniques in Intrusion Detection using Rapid Miner - Choudhary, Sanjeet; Namdeo, Varsha; Dwivedi, Abhijit (International Journal of Computer Sciences and Engineering, 2025)
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.