IoT Network Attack Detection using Supervised Machine Learning

Date

2021

Authors

Krishnan, Sundar
Neyaz, Ashar
Liu, Qingzhong

Journal Title

Journal ISSN

Volume Title

Publisher

International Journal of Artificial Intelligence and Expert Systems

Abstract

The use of supervised learning algorithms to detect malicious traffic can be valuable in designing intrusion detection systems and ascertaining security risks. The Internet of things (IoT) refers to the billions of physical, electronic devices around the world that are often connected over the Internet. The growth of IoT systems comes at the risk of network attacks such as denial of service (DoS) and spoofing. In this research, we perform various supervised feature selection methods and employ three classifiers on IoT network data. The classifiers predict with high accuracy if the network traffic against the IoT device was malicious or benign. We compare the feature selection methods to arrive at the best that can be used for network intrusion prediction

Description

Article originally published in International Journal of Artificial Intelligence and Expert Systems

Keywords

supervised learning, network attack detection, IoT, network forensics, network security

Citation

Kumar A, Neyaz A and Liu Q (2021). IoT Network Attack Detection using Supervised Machine Learning. International Journal of Artificial Intelligence and Expert Systems, 10(2): 18-32.