PhD Forum Abstract: Dynamic Inference on IoT Network Traffic using Programmable Telemetry and Machine Learning

IoT networks continue to expand in various domains, from smart homes and campuses to smart cities and critical infrastructures. Due to the lack of appropriate security measures embedded in IoT devices; they are increasingly becoming the target of sophisticated cyber-attacks. Existing machine :learning-based methods for monitoring and securing IoT networks are fragile, expensive, and inflexible, and hence unable to cost-effectively cope with the dynamic and complex nature of attacks. For my PhD thesis, the aim is to develop a robust; accurate, scalable, and cost-effective network monitoring solution for securing large IoT systems. To do so, we would require to dynamically measure and analyze certain portions of network traffic. Therefore, we employ programmable networking techniques to dynamically and selectively acquire necessary telemetry data (packets and/or flows of connected devices) fed to a collection of learning-based models (each specialized in certain protocols with specific granularity) to classify devices, monitor their activity; and detect: malicious behaviors. Our preliminary results show that the signature analysis of selected signaling packets from 26 real IoT devices yields a reasonably accurate inference by progressively collecting telemetry data at manageable processing costs.