Neuromorphic Continual Learning for Sequential Deployment of Nuclear Plant Monitoring Systems
Summary: arXiv:2604.18611v1 Announce Type: cross
Abstract: Anomaly detection in nuclear industrial control systems (ICS) requires continuous, energy-efficient monitoring across multiple subsystems that are often deployed at different stages of plant commissioning. When a conventional neural network is sequentially trained to monitor new subsystems, it catastrophically forgets previously learned anomaly patterns, a safety-critical failure mode. We present the first spiking neural network (SNN)-based anomaly detection system with continual learning for nuclear ICS, addressing both challenges simultaneously.
Introduction
In the rapidly evolving field of nuclear plant monitoring, the integration of advanced technologies is paramount for ensuring safety and efficiency. The recent advancements in spiking neural networks (SNNs) provide promising solutions for the critical task of anomaly detection. This article explores the latest research findings regarding a neuromorphic computing approach that offers continual learning capabilities specifically designed for nuclear industrial control systems.
Challenges in Anomaly Detection
Continuous and energy-efficient monitoring of nuclear industrial control systems presents unique challenges:
- Catastrophic Forgetting: Traditional neural networks often forget previously learned information when trained on new data, leading to safety risks in critical environments.
- Heterogeneous Sensor Inputs: Nuclear plants utilize multiple subsystems with varying sensor types and data formats, complicating the monitoring process.
Proposed Solution
The proposed system introduces several innovative features:
- Spike-Encoded Asynchronous Sensor Fusion: This method converts heterogeneous sensor streams into sparse spike trains, significantly enhancing data processing efficiency.
- Delta-Based Encoding: The encoding mechanism is tailored to the natural dynamics of each sensor, achieving an impressive 92.7% input sparsity.
Evaluation and Results
Five continual learning strategies were evaluated on the HAI 21.03 nuclear ICS security dataset across three subsystems: boiler, turbine, and water treatment.
- Strategies Tested: Sequential fine-tuning, Elastic Weight Consolidation (EWC), Synaptic Intelligence (SI), experience replay, and a hybrid EWC+Replay approach.
- Performance Metrics: The hybrid EWC+Replay method achieved an average F1 score of 0.979 with near-zero average forgetting rates (AF = 0.000 for a single seed; 0.035 ± 0.039 across three seeds).
- Operational Efficiency: This approach required 12.6 times fewer operations and an estimated 2.5 times less energy compared to traditional artificial neural networks.
- Attack Detection: The system successfully detected all tested attacks with a mean latency of just 0.6 seconds.
Conclusion
The findings of this research illustrate that neuromorphic computing can play a critical role in developing always-on, energy-efficient, and adaptable safety monitoring systems for next-generation nuclear facilities. The innovative solutions proposed not only address existing challenges but also pave the way for safer operational protocols in the nuclear industry.