A Comparison of Machine Learning Methods on Intrusion Detection Systems for Internet of Things
Anteng Widodo(1,4,*), Adi Wibowo(2), Budi Warsito(3)

1)Phd student of Doctor of Information System, School of Postgraduate Studies, Diponegoro University, Semarang, Indonesia
2)Department of Informatics, Faculty of Science and Mathematics, Diponegoro University, Semarang, Indonesia
3)Department of Statistics, Faculty of Science and Mathematics, Diponegoro University, Semarang, Indonesia
4)Department of Information System, Faculty of Engineering, Muria Kudus University, Gondangmanis, Bae, Kudus 59324, Indonesia

Abstract

In recent years, the internet of things is prevalent and widely used. The new problem with IoT is security, which needs to be considered carefully because of the technology heterogeneity. These threats can affect IoT performance- therefore, it is necessary for effective monitoring. This paper examines several machine learning methods in intrusion detection systems that possibly run on IoT. Random Forests and Decision Tree are employed in this study for performance comparison. The experimental results show that the Random Forest and Decision tree algorithms application produces good performance with a faster response time and possible running on IoT.

Keywords: Intrusion Detection Systems, Internet of Things, Machine learning

Topic: Computer Science