Privacy-Preserving Collaborative SDR Networks for Anomaly Detection

By offering unprecedented flexibility for wireless communications, Software-Defined Radio (SDR) is an enabling technology for distributed platforms such as Internet-of-Things (IoT) and cognitive radio. We propose a network of SDR devices with sensing and computation capabilities without relying on a centralized architecture to monitor and develop a detailed representation of network behavior in the radio frequency spectrum. This network representation provides a foundation for the task of anomaly detection for security purposes, in which SDRs collect the data needed to identify adversarial actors in a selected environment. We present and evaluate a novel distributed anomaly detection scheme for SDRs that applies Round Robin Learning (RRL) and Random Exchange Learning (REL) strategies to a type of machine learning model known as an autoencoder. In doing so, participating devices fill in gaps in a single model’s data representation, in order to build a model closer to that achieved through centralized training, while maintaining the advantages of distributed systems. The distributed architecture developed improves network robustness, decreases the amount of intranetwork communication, and preserves privacy of individual SDR subnetworks. Furthermore, it was found to consistently raise the average anomaly detection performance for groups of participating devices without exchanging any training data.