Securing Time in Energy IoT: A Clock-Dynamics-Aware Spatio-Temporal Graph Attention Network for Clock Drift Attacks and Y2K38 Failures

2026-01-30

AI Summary

This work addresses the vulnerability of time-integrity–based anomaly detection in energy Internet-of-Things (IoT) systems to attacks such as clock drift, time synchronization tampering, and Y2K38 overflow, which compromise the reliability of trusted timestamps. To counter this, the authors propose STGAT, a novel framework that integrates clock dynamics awareness into a spatiotemporal graph neural network. STGAT jointly models individual device time distortions and collective temporal consistency through drift-aware temporal embeddings, temporal self-attention, and graph attention mechanisms. Furthermore, it employs curvature regularization to geometrically separate normal and anomalous behaviors in the latent space. Experimental results on energy IoT datasets with controlled perturbations demonstrate that STGAT achieves a detection accuracy of 95.7%, significantly outperforming baseline methods (Cohen’s d > 1.8, p < 0.001), while reducing detection latency by 26%—equivalent to only 2.3 time steps.

Technology Category

• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Application Domains: Internet of Things, Sensor Networks & Smart Cities

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Web Mining and Content Analysis: Web data provenance, reliability, and authenticity

Abstract

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.

Problem

• clock drift
• Y2K38 failure
• time synchronization
• temporal inconsistency
• energy IoT

Innovation

• Spatio-Temporal Graph Attention Network
• Clock Drift Detection
• Y2K38 Overflow
• Time Integrity
• Curvature-Regularized Representation