The integrity of time in distributed Internet of Things (IoT) devices is crucial for reliable operation in energy cyber-physical systems, such as smart grids and microgrids. However, IoT systems are vulnerable to clock drift, time-synchronization manipulation, and timestamp discontinuities, such as the Year 2038 (Y2K38) Unix overflow, all of which disrupt temporal ordering. Conventional anomaly-detection models, which assume reliable timestamps, fail to capture temporal inconsistencies. This paper introduces STGAT (Spatio-Temporal Graph Attention Network), a framework that models both temporal distortion and inter-device consistency in energy IoT systems. STGAT combines drift-aware temporal embeddings and temporal self-attention to capture corrupted time evolution at individual devices, and uses graph attention to model spatial propagation of timing errors. A curvature-regularized latent representation geometrically separates normal clock evolution from anomalies caused by drift, synchronization offsets, and overflow events. Experimental results on energy IoT telemetry with controlled timing perturbations show that STGAT achieves 95.7% accuracy, outperforming recurrent, transformer, and graph-based baselines with significant improvements (d > 1.8, p < 0.001). Additionally, STGAT reduces detection delay by 26%, achieving a 2.3-time-step delay while maintaining stable performance under overflow, drift, and physical inconsistencies.

翻译：分布式物联网设备中时间的完整性对于能源信息物理系统（如智能电网和微电网）的可靠运行至关重要。然而，物联网系统易受时钟漂移、时间同步操纵以及时间戳不连续性（例如2038年Unix溢出问题）的影响，这些都会破坏时间顺序。传统异常检测模型假设时间戳可靠，无法捕捉时间不一致性。本文提出STGAT（时空图注意力网络），这是一个对能源物联网系统中时间失真与设备间一致性进行建模的框架。STGAT结合了漂移感知的时间嵌入和时间自注意力机制，以捕捉单个设备上被破坏的时间演化过程，并利用图注意力机制对时序错误的空间传播进行建模。通过曲率正则化的潜在表示，在几何上将正常的时钟演化与由漂移、同步偏移和溢出事件引起的异常分离开来。在受控时序扰动下的能源物联网遥测数据上的实验结果表明，STGAT实现了95.7%的准确率，显著优于循环、Transformer和图基线模型（d > 1.8, p < 0.001）。此外，STGAT将检测延迟降低了26%，实现了2.3个时间步长的延迟，同时在溢出、漂移和物理不一致性条件下保持稳定的性能。