PhaseNet++: 一种基于相位感知的频域工业控制系统异常检测方法
PhaseNet++: Phase-Aware Frequency-Domain Anomaly Detection for Industrial Control Systems via Phase Coherence Graphs
AI总结：
本文提出PhaseNet++，通过相位一致性图网络在频域中检测工业控制系统异常，利用相位信息提升检测性能，实验表明其在SWaT基准测试中取得高F1分数和ROC-AUC值。
摘要：
多变量时间序列异常检测在工业控制系统中日益受到关注，由于对关键基础设施的网络物理攻击威胁增加。现有方法通过图神经网络和Transformer建模传感器间的关系，但忽略了时间频率变换产生的相位谱。本文提出PhaseNet++，一种基于滑动传感器窗口的短时傅里叶变换（STFT）的频域自编码器，保留幅度和相位谱。相位一致性指数（PCI）总结频率桶间的相位一致性为连续邻接矩阵，指导图注意力网络在相位同步传感器间传播信息。传感器-令牌Transformer编码器捕捉系统结构，双头解码器通过圆形和相干性目标联合重建幅度和相位。在Secure Water Treatment（SWaT）基准测试中，PhaseNet++取得F1分数90.98%、ROC-AUC 95.66%和平均精度91.51%。消融研究显示，相位感知前端和PCI图模块仅增加264,816个参数，表明相位归纳偏差轻量。尽管绝对F1分数略低于所有最近原始值方法，本文工作是首个系统研究工业控制系统相位域异常检测的工作。
英文摘要：
Multivariate time series anomaly detection in ICS has attracted growing attention due to the increasing threat of cyber-physical attacks on critical infrastructure. State-of-the-art methods model inter-sensor relationships from raw time-domain amplitude values, using graph neural networks, Transformers. However, these methods discard the phase spectrum produced by time frequency transformations, We argue that phase information constitutes a complementary and previously overlooked detection modality for ICS anomaly detection. We present PhaseNet++, a frequency-domain autoencoder that operates on the Short-Time Fourier Transform (STFT) of sliding sensor windows, retaining both magnitude and phase spectra. A Phase Coherence Index (PCI), inspired by the Phase Locking Value from neuroscience, summarizes pairwise phase consistency across frequency bins into a continuous adjacency matrix. This matrix guides a graph attention network that propagates information preferentially among phase-synchronized sensors. A sensor-token Transformer encoder captures system-wide structure, and a dual-head decoder reconstructs magnitude and phase jointly via circular and coherence-aware objectives. Evaluated on the Secure Water Treatment (SWaT) benchmark, PhaseNet++ achieves an F1-score of 90.98%, ROC-AUC of 95.66%, and average precision of 91.51%. Ablation studies show that the phase-aware front-end and PCI graph module together add only 264,816 parameters, demonstrating that the phase inductive bias is lightweight. While the absolute F1-score is second best than that of all recent raw-value methods evaluated under different protocols, we position this work as the first systematic study of phase-domain anomaly detection for ICS.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
