A Data Analytics System for Network Intrusion Detection Using Decision Tree

Terungwa Simon Yange^1, , Oluoha Onyekwere² and Yakubu Musa Abdulmuminu³

¹Department of Mathematics/Statistics/Computer Science, University of Agriculture, Makurdi, Nigeria

²Department of Computer Science, University of Nigeria, Nsukka

³Department of Computer Science, Federal University Lokoja, Lokoja, Nigeria

Cite this paper:
Terungwa Simon Yange, Oluoha Onyekwere and Yakubu Musa Abdulmuminu. A Data Analytics System for Network Intrusion Detection Using Decision Tree. Journal of Computer Sciences and Applications. 2020; 8(1):21-29. doi: 10.12691/jcsa-8-1-4

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.e., 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.

Keywords:
data analytics decision tree intrusion detection attack intruder

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