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IoT Network Attack Detection using Supervised Machine Learning
Sundar Krishnan, Ashar Neyaz, Qingzhong Liu
Pages - 18 - 32     |    Revised - 31-05-2021     |    Published - 30-06-2021
Volume - 10   Issue - 2    |    Publication Date - June 2021  Table of Contents
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KEYWORDS
Supervised Learning, Network Attack Detection, IoT, Network Forensics, Network Security.
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.
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Mr. Sundar Krishnan
Department of Computer Science, Sam Houston State University, Huntsville, TX - United States of America
skrishnan@shsu.edu
Mr. Ashar Neyaz
Department of Computer Science, Sam Houston State University, Huntsville, TX - United States of America
Dr. Qingzhong Liu
Department of Computer Science, Sam Houston State University, Huntsville, TX - United States of America