Demo: P4 Based In-network ML with Federated Learning to Secure and Slice IoT Networks
Chamara Madarasingha, Thilini Dahanayaka, Kanchana Thilakarathna, Suranga Seneviratne, Young Choon Lee, Salil S. Kanhere, Albert Y. Zomaya, Aruna Prasad Seneviratne, Phil Ridley · 2024
Recent cyberattacks have increasingly targeted distributed networking environments like IoT networks. To detect these attacks, hidden under network traffic encryption, many centralized Machine Learning (ML) based solutions have been introduced, which are not well suited for IoT networks. This work proposes PIFL a practical approach to secure IoT networks by combining federated learning, in-network ML using P4-enabled devices, software-defined networks, and binarized neural networks. PIFL detects compromised edge devices and isolates them into separate network slices based on trust parameters derived from their behavior. We demonstrate the feasibility of PIFL using an experimental testbed with three intelligent network devices and seven IoT devices implemented on Raspberry Pi devices.