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.

翻译：核工业控制系统（ICS）中的异常检测需要跨多个子系统进行持续、节能的监控，而这些子系统通常在不同机组调试阶段部署。传统神经网络在顺序训练以监控新子系统时，会灾难性地遗忘先前学习到的异常模式，这是一种关键安全失效模式。本文提出首个基于脉冲神经网络（SNN）的核ICS持续学习异常检测系统，同时解决上述两个挑战。我们引入脉冲编码异步传感器融合方法，这是一种基于增量编码的方案，能将异构传感器数据流转换为稀疏脉冲序列，其脉冲频率由各传感器的自然动态决定，实现了92.7%的输入稀疏度。我们在HAI 21.03核ICS安全数据集上，针对三个顺序部署的子系统（锅炉、汽轮机、水处理），评估了包括顺序微调、弹性权重巩固（EWC）、突触智能（SI）、经验回放及混合EWC+回放在内的五种持续学习策略。其中混合EWC+回放方法平均F1分数达0.979，近乎零平均遗忘（单次种子AF=0.000，三次种子AF=0.035±0.039），同时计算量仅为等效人工神经网络的12.6倍（基于已发布硬件规格估算能耗降低2.5倍）。该系统检测所有测试攻击的平均延迟为0.6秒。研究结果表明，神经形态计算为下一代核设施实现常开型、节能且可自适应安全监控提供了可行路径。