European Academic Research ISSN 2286-4822
ISSN-L 2286-4822
Impact Factor: 3.4546 (UIF)
DRJI Value : 5.9 (B+)
Article Details :
Article Name :
A Comparative Study on Machine Learning Tools Using WEKA and Rapid Miner with Classifier Algorithms C4.5 and Decision Stump for Network Intrusion Detection
Author Name :
Wathq Ahmed Ali Saeed Kawelah, Ahmed Salah Eldin Abdala
Publisher :
Bridge Center
Article URL :
Abstract :
Intrusion detection system dealing with the huge amount of data that include repeated irrelevant cause slow process of testing, training and higher learning resource consumption as well as the vulnerability of the detection rate. Data mining techniques are being applied in the construction of intrusion detection systems to protect computing resources against unauthorized access. In this paper, we have done a comparative study on machine learning tools using WEKA and Rapid Miner with two classifier algorithms C4.5 and Decision Stump for Network Intrusion Detection to measure the accuracy, sensitivity and precision. The results of the experiments using the KDD’ 99 attack dataset and select seven features, The results show the best tools Rapid Miner for the accuracy and precision, while the best algorithms is C4.5.
Keywords :
Data Mining Tools; WEKA; Rapid Miner; C4.5 and Decision Stump

New Launched Project
Recommend & Share