“Machine Learning Based Device Type Classification for IoT device Continuous and Re-Authentication”

Today, the use of Internet of Things (IoT) devices is higher than ever and it is growing rapidly. Many IoT devices are usually manufactured by home appliance manufacturers where the security and the privacy is not the foremost concern. When an IoT device is connected to a network, currently, there does not exist a strict authentication method that verifies the identity of the device, allowing any rogue IoT device to authenticate to an access point. This thesis addresses the issue by introducing methods for continuous and re-authentication of static and dynamic IoT devices respectively. We introduce mechanisms and protocols for authenticating a device in a network through leveraging Machine Learning (ML) to classify not only if the device is IoT or not but also the type of IoT device attempting to connect to the network with an accuracy over 95%. Furthermore, we compare different types of machine learning classifiers to best estimate the types of IoT device and use them to develop a stricter and more efficient method for authentication.

Committee:
Dr. Nirnimesh Ghose,
Dr. Byrav Ramamurthy,
Dr. Lisong Xu