Robust Wireless Network Anomaly Detection with Collaborative Adversarial Autoencoders

Anomaly detection is often deployed in centralised systems, for which critical failure points exist. However, the rising availability of low-cost, wireless-connected devices introduces opportunities for new anomaly detection techniques that leverage more robust topologies. In this paper, we propose a novel collaborative training scheme for anomaly detection models that involves sharing machine learning models amongst devices for incremental training. Using the Adversarial Autoencoder architecture, pseudo-rehearsal, and gossip-based communication, our framework provides all participating devices with a structured representation of other devices' data, so that training can continue even in the event of a device failure, with a 43% smaller performance degradation than state of the art alternatives. Under both optimal conditions and those with device failure, our model consistently exhibits better anomaly detection performance.