A Comparative Study for Machine Learning Tools Using WEKA and Rapid Miner with Classifier Algorithms Random Tree and Random Forest for Network Intrusion Detection

Authors : Wathq Ahmed Ali Saeed Kawelah, Ahmed Salah Eldin Abdala.

Volume/Issue : Volume 4 - 2019, Issue 4 - April

Google Scholar : https://goo.gl/DF9R4u

Scribd : https://bit.ly/2HNItaa

The internet world expands day by day as well as threats related to it. Nowadays, Cyber-attacks often happen more than a decade ago. Intrusion detection is one of the most popular search area that provides various technologies and security techniques for detecting cyber-attacks. Different data extraction tools learn to algorithms that help in the implementation of the Learn to build Identities. In this paper, we have done a comparative study for machine learning tools using WEKA and Rapid Miner with two algorithms Random Tree and Random Forest for network intrusion detection. These can be used to implement intrusion detection techniques based on data mining. Analysis of the initial results of two different machine learning tools WEKA and Rapid Miner is carried out using KDD’ 99 attack dataset and results are the best tools is WEKA, while the best algorithms is Random Forest.

Keywords : Data Mining Tools; WEKA; Rapid Miner; Random Tree and Random Forest.


Paper Submission Last Date
31 - December - 2021

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