基于神经形态时间嵌入与混合SNN XGBoost的鲁棒工业网络物理分类（面对机器遗忘攻击）
Robust Industrial Cyber Physical Classification Using Neuromorphic Temporal Embeddings and Hybrid SNN XGBoost Under Machine Unlearning Attacks
- RMIT University（皇家墨尔本理工大学）
- Powercor（Powercor公司）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
提出混合SNN与XGBoost架构，利用神经形态时间编码实现轻量级鲁棒入侵检测，在电力系统数据集上达99.9%准确率，并有效抵御机器遗忘攻击。
AI中文摘要：
配电网络的数字化增加了电网基础设施遭受网络攻击的风险。然而，现有的入侵检测系统（IDSs）通常依赖计算成本高昂的深度学习模型，难以在边缘部署。周期性重训练也使这些系统面临机器遗忘攻击，其中选择性数据删除会降低检测性能。我们提出了一种混合脉冲神经网络（SNN）和XGBoost架构，该架构将高效的时间编码与轻量级分类器相结合，并对此类攻击提供结构性的鲁棒性。SNN仅在干净数据上训练一次，用作固定的特征提取器，而模型更新期间仅重训练XGBoost分类器。在两个真实世界的公共电力系统数据集上的评估表明，所提方法在同步相量数据集上达到99.9%的准确率（F1宏平均0.999），在MSU/ORNL数据集上达到95.0%的准确率（F1宏平均0.943），优于独立基线。在选择性标签翻转攻击下，混合模型在10%投毒时仅损失0.9%的F1宏平均，并将目标类别崩溃从60%延迟至70%投毒（与原始模型相比）。这些结果表明，神经形态时间编码能够提供准确的网络攻击检测，并增强网络物理系统对数据投毒的鲁棒性。
英文摘要：
The digitalisation of electrical distribution networks has increased the exposure of power-grid infrastructure to cyber attacks. Existing intrusion detection systems (IDSs), however, often rely on computationally expensive deep learning models that are difficult to deploy at the edge. Periodic retraining also exposes these systems to machine unlearning attacks, where selective data removal can degrade detection performance. We propose a hybrid Spiking Neural Network (SNN) and XGBoost architecture that combines efficient temporal encoding with a lightweight classifier and provides structural resilience to such attacks. The SNN is trained once on clean data and used as a fixed feature extractor, while only the XGBoost classifier is retrained during model updates. Evaluated on two real-world public power-system datasets, the proposed method achieves 99.9\% accuracy (F1-macro 0.999) on the Synchrophasor dataset and 95.0\% accuracy (F1-macro 0.943) on the MSU/ORNL dataset, outperforming standalone baselines. Under selective label-flipping attacks, the hybrid model loses only 0.9\% F1-macro at 10\% poisoning and delays target-class collapse from 60\% to 70\% poisoning compared with raw models. These results demonstrate that neuromorphic temporal encoding can provide both accurate cyber-attack detection and improved resilience to data poisoning in cyber-physical systems.
