Author(s):
1. HAYEL KHAFAJEH:
Faculty of Information Technology, Zarqa University, Zarqa, Jordan
Abstract:
Nowadays, network security has been received more attention from researchers. Intrusion detection systems (IDSs) serves as an essential element of network security. In order to increase the network's security, machine-learning algorithms may be utilized for the detection and prevention of the attacks that launched against the network. The researcher of this study used LightGBM's algorithm for training a model in order to detect several types of network attacks. The proposed approach was compared with classical machine learning in terms of performance on the same dataset. The experimental results show that the proposed approach achieves a detection rate of 97.4% with a false-positive rate of 0.9%.
Page(s):
825-835
DOI:
DOI not available
Published:
Journal: Journal of Theoretical and Applied Information Technology, Volume: 98, Issue: 5, Year: 2020
Keywords:
machine learning
,
IDS
,
LGBM
References:
References are not available for this document.
Citations
Citations are not available for this document.