Machine learning-based DNS traffic monitoring for securing IoT networks
Mehwish Weqar, Shabana Mehfuz, Dhawal Gupta · 2023
Internet of things is the vast network of connected sensory objects, which senses the physical environment and sends data over a wired or wireless network. These devices are resource-constrained in terms of computation power, storage capacity, battery backup, and so on. The IoT network is dynamic in nature, which increases at a very fast pace, including/connecting billions of devices. Depending on the wide range of applications, the type of software and hardware of these IoT devices vary, giving rise to a heterogeneous environment. Most of the time these devices are placed in an unmanned environment where they have to autonomously get themselves discovered and registered in order to send data and support services. All the above factors make them more vulnerable to various threats present in cyberspace. To render these IoT networks secure and free from risks, many techniques have been proposed over a period of time, which focus on implementing machine learning techniques for monitoring and analyzing DNS network traffic. In this chapter we are going to examine and evaluate the recently proposed DNS traffic monitoring techniques for IoT networks and then examine how machine learning techniques are applied to scrutinize and control illegitimate DNS traffic in IoT networks. In conclusion, a discussion on the possible enhancement and future directions toward improving IoT network security has been presented.