A Framework for In-network Inference using P4

Machine Learning (ML) has been widely used in network security monitoring. Although, its application to data intensive use cases and those requiring ultra-low latency remains challenging. This is due to the large amounts of network data and the need of transferring data to a central location hosting analysis services. In this paper, we present a framework to perform in-network analysis by offloading ML inference tasks from end servers to P4-capable programmable network devices. This helps reduce transfer latency and, thus, allows faster attack detection and mitigation. It also improves privacy since the data is processed at the networking devices. The paper also presents an experimental use-case of the framework to classify network traffic, and to early detect and rapidly mitigate against IoT malicious traffic.