期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2019
卷号：10
期号：12
页码：627-634
出版社：Science and Information Society (SAI)
摘要：The Internet of Things (IoT) combines hundreds
of millions of devices which are capable of interaction with
each other with minimum user interaction. IoT is one of the
fastest-growing areas in of computing; however, the reality is
that in the extremely hostile environment of the internet, IoT
is vulnerable to numerous types of cyberattacks. To resolve
this, practical countermeasures need to be established to secure
IoT networks, such as network anomaly detection. Regardless
that attacks cannot be wholly avoided forever, early detection
of an attack is crucial for practical defense. Since IoT devices
have low storage capacity and low processing power, traditional
high-end security solutions to protect an IoT system are not
appropriate. Also, IoT devices are now connected without human
intervention for longer periods. This implies that intelligent
network-based security solutions like machine learning solutions
must be developed. Although many studies in recent years have
discussed the use of Machine Learning (ML) solutions in attack
detection problems, little attention has been given to the detection
of attacks specifically in IoT networks. In this study, we aim
to contribute to the literature by evaluating various machine
learning algorithms that can be used to quickly and effectively
detect IoT network attacks. A new dataset, Bot-IoT, is used
to evaluate various detection algorithms. In the implementation
phase, seven different machine learning algorithms were used,
and most of them achieved high performance. New features were
extracted from the Bot-IoT dataset during the implementation
and compared with studies from the literature, and the new
features gave better results.
关键词：Network anomaly detection; machine learning; Internet of Things (IoT); cyberattacks; bot-IoT dataset
