类脑持续学习用于核电厂监控系统的序列部署
Neuromorphic Continual Learning for Sequential Deployment of Nuclear Plant Monitoring Systems
AI总结：
本文提出基于脉冲神经网络的持续学习异常检测系统，解决核工业控制系统中多子系统连续部署的持续学习与能量效率问题，通过异步传感器融合与混合EWC+Replay方法实现高精度检测。
摘要：
核工业控制系统异常检测需要在多个子系统连续部署过程中实现高效、低能耗的监控。传统神经网络在连续训练新子系统时会灾难性遗忘先前学习的异常模式，导致安全关键性故障。本文首次提出基于脉冲神经网络（SNN）的持续学习异常检测系统，同时解决这两个挑战。我们的方法引入了基于脉冲的异步传感器融合，一种基于delta的编码方式，将异构传感器流转换为稀疏脉冲序列，其速率由每个传感器的自然动态决定，实现了92.7%的输入稀疏性。我们评估了五种持续学习策略，包括顺序微调、弹性权重巩固（EWC）、突触智能（SI）、经验回放以及混合EWC+Replay方法，在HAI 21.03核工业控制系统安全数据集上跨三个连续部署的子系统（锅炉、涡轮机、水处理）进行测试。混合EWC+Replay方法在平均F1得分为0.979时，平均遗忘率接近零（AF=0.000单种子；0.035±0.039跨三个种子），且所需操作仅为等效人工神经网络的12.6倍（基于已发布硬件规格估计约为2.5倍的能耗）。该系统在0.6秒平均延迟内检测所有测试攻击。这些结果表明，类脑计算为下一代核设施提供了一条可行的持续、低能耗和适应性安全监控路径。
英文摘要：
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. Our approach introduces spike-encoded asynchronous sensor fusion, a delta-based encoding that converts heterogeneous sensor streams into sparse spike trains at rates dictated by each sensor's natural dynamics, achieving 92.7% input sparsity. We evaluate five continual learning strategies, including sequential fine-tuning, Elastic Weight Consolidation (EWC), Synaptic Intelligence (SI), experience replay, and a hybrid EWC+Replay approach, on the HAI 21.03 nuclear ICS security dataset across three sequentially deployed subsystems (boiler, turbine, water treatment). The hybrid EWC+Replay method achieves an average F1 score of 0.979 with near-zero average forgetting (AF = 0.000 single seed; 0.035 +/- 0.039 across three seeds), while requiring 12.6x fewer operations (an estimated 2.5x in energy based on published hardware specifications) than an equivalent artificial neural network. The system detects all tested attacks with a mean latency of 0.6 seconds. These results demonstrate that neuromorphic computing offers a viable path toward always-on, energy-efficient, and adaptable safety monitoring for next-generation nuclear facilities.
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