Advancing IoT Security with Tsetlin Machines: A Resource-Efficient Anomaly Detection Approach

The number of IoT devices are rapidly increasing, and the nature of the devices leave them vulnerable to attacks. As of today there are no general security solutions that meet the requirements of running with limited resources on devices with a large variety of use cases. Traditional AI models are able to classify and distinguish between benign and malignant network traffic. However, they require more resources than IoT devices can provide, and cannot train on-chip once deployed. This thesis introduces the Tsetlin Machine as a potential solution to this problem. As a binary, propositional logic model, the Tsetlin Machine is compatible with hardware and can perform predictions in near real-t…