Open AccessResearch on the Performance of Machine Learning Algorithms for Intrusion Detection System
Author(s) -
Yue Li,
Wenqian Xu,
Qiuqi Ruan
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1693/1/012109
Subject(s) - intrusion detection system , computer science , hacker , normalization (sociology) , machine learning , intrusion , artificial intelligence , set (abstract data type) , intrusion prevention system , network security , algorithm , data mining , computer security , anthropology , programming language , geology , geochemistry , sociology
Intrusion Detection System (IDS) is a critical approach to ensure network system security. Currently, network attacks are complicated and volatile. Moreover, hackers are also more inclined to adopt new attack techniques to obtain users’ privacy. Under this circumstance, the intelligent intrusion detection system has become the primary approach to detect hackers’ attacks. In this study, intelligent intrusion detection models applying the novel UNSW-NB15 data set as well as various machine learning algorithms are investigated. Furthermore, data set is pre-processed through using one-hot encoding and normalization in our experiment. Subsequently, the performance comparison of six different types of machine learning algorithms in intrusion detection tasks was implemented. The experimental results reveal that in the complex and changeable network traffic data, machine learning technology has presented desired performance.