Diversion Detection in Partially Observed Nuclear Fuel Cycle Networks

TL;DR

This paper proposes a method to detect anomalies in partially observed nuclear fuel cycle networks by estimating true network traffic and identifying significant deviations, even with incomplete data, demonstrating superior performance over existing methods.

Contribution

It introduces estimators for latent network traffic and anomalies in partially observed networks, showing effective detection without full network reconstruction.

Findings

Estimators outperform existing alternatives in simulations.

Perfect network reconstruction is unnecessary for anomaly detection.

Effective detection achieved through global perturbation analysis.

Abstract

A nuclear fuel cycle contains several facilities with different purposes such as mining, conversion, enrichment, and fuel rod fabrication. These facilities form a network, which is naturally sparse in the number of connections (i.e., edges) since not every facility directly interacts with all the others. Given the knowledge of a network baseline, we are interested in detecting anomalous activities in this network, which may signal the diversion of nuclear materials. Anomalies can take the form of a new or missing edge or abnormal rates of interaction. However, often it is not possible to observe the entire network traffic directly due to some constraints such as cost, physical limitations, or laws. By treating the unobserved network traffic as latent variables, we propose estimators for the true network traffic, including the anomalous activity, to use in testing for significant deviations from the baseline. We provide simulation results of a simple network of facilities and show that our estimators have superior performance over existing alternatives. Additionally, we establish that while a good estimate of the network traffic is necessary, perfect reconstruction is not required to effectively detect anomalous network activity. Instead it suffices to detect perturbations within the network at an aggregate or global scale.