Optimizing authentication performance for industrial IoT devices

Abstract

With the rising prevalence of industrial Internet of Things (IoT) devices, it has become more important to ensure that IoT devices are secure. A method that attackers use to gain access to the IoT devices is to spoof themselves as the IoT server. Traditional authentication methods need a lot of memory resources, which is unable to meet the needs of lightweight IoT devices. The idea of the new authentication protocol is to use the historical data exchanged between an IoT device (verifier) and a server (prover) as a second authentication factor, in addition to the conventional factor of a shared secret key. The new authentication method needs more memory resources of server instead of IoT devices, which is what people want. However, the historical data authentication factor is considered a big dataset and the retrieve of selected pieces of data from a large data pool usually encounters long latency if the algorithm and implementation are not well optimized. To reduce latency, three methods are proposed in this dissertation. After presenting the new authentication method using historical data and optimizing it, a real system is implemented. Because the IoT devices are normally unable to verify the legality of the server, BeagleBone Black (BBB) (a kind of single board computer) is used to verify. Thus, the communication between the IoT device and BBB and between BBB and server are key problems. A structure named “bridge” is built and in order to insert verifier process, ip_queue in Linux is adopted. Thus, an optimized authentication process using historical data can be run in this system.