Electrical and Electronic Engineering - Research Publications

Permanent URI for this collection

Now showing 1 - 3 of 3

Robust Wireless Network Anomaly Detection with Collaborative Adversarial Autoencoders

Katzef, M ; Cullen, AC ; Alpcan, T ; Leckie, C (Institute of Electrical and Electronics Engineers, 2023)

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.
Wireless Network Simulation to Create Machine Learning Benchmark Data

Katzef, M ; Cullen, AC ; Alpcan, T ; Leckie, C ; Kopacz, J (IEEE, 2022-01-01)
Generative Adversarial Networks for anomaly detection on decentralised data

Katzefa, M ; Cullen, AC ; Alpcan, T ; Leckie, C (PERGAMON-ELSEVIER SCIENCE LTD, 2022)