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.

CALL FOR PAPERS


Paper Submission Last Date
31 - March - 2024

Paper Review Notification
In 1-2 Days

Paper Publishing
In 2-3 Days

Video Explanation for Published paper

Never miss an update from Papermashup

Get notified about the latest tutorials and downloads.

Subscribe by Email

Get alerts directly into your inbox after each post and stay updated.
Subscribe
OR

Subscribe by RSS

Add our RSS to your feedreader to get regular updates from us.
Subscribe