Zeadally, S. and M. Tsikerdekis (2019).
Securing Internet of Things (IoT) with machine learning
International Journal of Communication Systems. doi: 10.1002/dac.4169.
Impact Factor: 1.278
Advances in hardware, software, communication, embedding computing technologies along with their decreasing costs and increasing performance have led to the
emergence of the Internet of Things (IoT) paradigm. Today, several billions of
Internet-connected devices are part of the IoT ecosystem. IoT devices have become
an integral part of the Information Communication Technology (ICT) infrastructure
that supports many of our daily activities. The security of these IoT devices has been
receiving a lot of attention in recent years. Another major recent trend is the amount
data that is being produced every day which has reignited interest in technologies
such as machine learning and artificial intelligence. We investigate the potential of
machine learning techniques in enhancing the security of IoT devices. We focus on
the deployment of supervised, unsupervised learning techniques, as well as reinforcement learning for both host-based and network-based security solutions in the IoT
environment. Finally, we discuss some of the challenges of machine learning techniques that need to be addressed in order to effectively implement and deploy them
so that they can better protect IoT devices.