A One-Class Explainable AI Framework for Identification of Non-Stationary Concurrent False Data Injections in Nuclear Reactor Signals

TL;DR

This paper introduces an explainable AI framework using recurrent neural networks and residual analysis to detect and interpret non-stationary false data injections in nuclear reactor signals, even with limited training data.

Contribution

It presents a novel XAI approach combining RNNs, residual analysis, and rule-based correlations for real-time detection of complex false data injections in nuclear reactors.

Findings

Detection accuracy over 0.93

False positive rate below 0.01

Effective differentiation from process anomalies

Abstract

The transition of next generation advanced nuclear reactor systems from analog to fully digital instrumentation and control will necessitate robust mechanisms to safeguard against potential data integrity threats. One challenge is the real-time characterization of false data injections, which can mask sensor signals and potentially disrupt reactor control systems. While significant progress has been made in anomaly detection within reactor systems, potential false data injections have been shown to bypass conventional linear time-invariant state estimators and failure detectors based on statistical thresholds. The dynamic, nonlinear, multi-variate nature of sensor signals, combined with inherent noise and limited availability of real-world training data, makes the characterization of such threats and more importantly their differentiation from anticipated process anomalies particularly challenging. In this paper, we present an eXplainable AI (XAI) framework for identifying non-stationary concurrent replay attacks in nuclear reactor signals with minimal training data. The proposed framework leverages progress on recurrent neural networks and residual analysis coupled with a modified SHAP algorithm and rule-based correlations. The recurrent neural networks are trained only on normal operational data while for residual analysis we introduce an adaptive windowing technique to improve detection accuracy. We successfully benchmarked this framework on a real-world dataset from Purdue's nuclear reactor (PUR-1). We were able to detect false data injections with accuracy higher than 0.93 and less than 0.01 false positives, differentiate from expected process anomalies, and to identify the origin of the falsified signals.