kumoh national institute of technology
Networked Systems Lab.

Gabriel Chukwunonso Amaizu , Cosmas Ifeanyi Nwakanma , Jae-Min Lee and Dong-Seong Kim "Investigating Network Intrusion Detection Datasets using Machine Learning Algorithm", ICTC - 2020 (A)[N12]
By : Gabriel
Date : 2020-06-07
Views : 82

This paper presents a machine learning based comparison of three major network intrusion datasets using a python based Artificial Neural Network (ANN) code. Theres been a series of datasets with regards to network intrusion detection in recent years, and a significant number of studies
has also been carried out using these datasets. In this paper we aim to explore these datasets, showing the capability of our code to model accurately their capabilities for network intrusion and detection. Results showed that our code performed best for NSL-KDD, followed by UNSW-NB15 and CSE-CICIDS2018 respectively. Model accuracy achieved for these datasets were NSL-KDD (97.89%), UNSW-NB15 (89.99%), and CSE-CICIDS2018 (76.47%) was achieved.