@article{jcsa2020814,
author={{Yange, Terungwa Simon and Onyekwere, Oluoha and Abdulmuminu, Yakubu Musa},
title={A Data Analytics System for Network Intrusion Detection Using Decision Tree},
journal={Journal of Computer Sciences and Applications},
volume={8},
number={1},
pages={21--29},
year={2020},
url={http://pubs.sciepub.com/jcsa/8/1/4},
issn={2328-725X},
abstract={Network intrusion detection systems are becoming an important tool for information security and technology world. Given the rise of attacks across the network, there is a pressing need to develop an improved security system to combat these growing threats on the computer network. The quality of an intrusion detection system is determined by the number of attacks its able to classify correctly. This research developed a data analytics system for network intrusion detection to combat the ever growing threats as well as classify them so as to ease the task of data scientists and network administrators. Decision tree algorithm and python programming language were used. KDD¡¯99 was used as the data source. Decision tree assists the network administrator to decide about the incoming traffic, <i>i.e., </i>whether the coming data is malicious or not by providing a model that separates malicious and non-malicious traffic. It allows taking less number of attributes and provides acceptable accuracy in reasonable account of time. From the results of the experiments, it is concluded that the system is more efficient with respect to finding attacks in the network with less number of features and it takes less time to construct the model. Also, the efficiency of the system has little or no regards for the size of the dataset and the number of features used to construct the decision tree.},
doi={10.12691/jcsa-8-1-4}
publisher={Science and Education Publishing}
}