A User Authentication Model for IoT Networks Based on App Traffic Patterns

Abstract

While access to a variety of Internet of Things (IoT) networks can be achieved through end-user devices such as smartphones or tablets, these devices are susceptible to theft or loss, and their use by illicit users could lead to unauthorized access to IoT networks, thus allowing access to user information. Hence, an effective authentication mechanism that continuously authenticates users in the background is required in order to detect unauthorized access. As most access to IoT devices is achieved through end-user devices, a rich set of information can be extracted and used in the background to continuously authenticate users without requiring further user intervention. Several studies have examined mobile app usage in the broader population but failed to consider network traffic patterns during app access for user authentication. This paper presents a model to authenticate users based on the network traffic patterns of accessed apps, with an average F-measure of 95.5%. Overall, the preliminary results are promising and show the effectiveness and usability of the proposed model.