# Securing Time Integrity in Energy IoT Against Clock Drift and Y2K38 Failures

arXiv: https://arxiv.org/abs/2601.23147 · HTML: https://mlsanity.com/paper/2601.23147/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 67 references that are in the map.
- First posted on arXiv 30 Jan 2026.

## Builds on (most cited first, 16 of 16)

- [Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy](https://mlsanity.com/paper/2110.02642/) (2021)
- [Multivariate Time-series Anomaly Detection via Graph Attention Network](https://mlsanity.com/paper/2009.02040/) (2020)
- [A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection](https://mlsanity.com/paper/2307.03759/) (2023)
- [Learning Graph Structures with Transformer for Multivariate Time Series Anomaly Detection in IoT](https://mlsanity.com/paper/2104.03466/) (2021)
- [Rethinking Irregular Time Series Forecasting: A Simple yet Effective Baseline](https://mlsanity.com/paper/2505.11250/) (2025)
- [MST-GAT: A Multimodal Spatial-Temporal Graph Attention Network for Time Series Anomaly Detection](https://mlsanity.com/paper/2310.11169/) (2023)
- [Positional Encoding in Transformer-Based Time Series Models: A Survey](https://mlsanity.com/paper/2502.12370/) (2025)
- [Graph Spatiotemporal Process for Multivariate Time Series Anomaly Detection with Missing Values](https://mlsanity.com/paper/2401.05800/) (2024)
- [Improving Interpretability via Regularization of Neural Activation Sensitivity](https://mlsanity.com/paper/2211.08686/) (2022)
- [Multi-Source Data Fusion for Cyberattack Detection in Power Systems](https://mlsanity.com/paper/2101.06897/) (2021)
- [The Syncline Model -- Analyzing the Impact of Time Synchronization in Sensor Fusion](https://mlsanity.com/paper/2209.01136/) (2022)
- [Improving Robustness Against Adversarial Attacks with Deeply Quantized Neural Networks](https://mlsanity.com/paper/2304.12829/) (2023)
- [Think Fast: Real-Time IoT Intrusion Reasoning Using IDS and LLMs at the Edge Gateway](https://mlsanity.com/paper/2511.18230/) (2025)
- [Tracking Real-time Anomalies in Cyber-Physical Systems Through Dynamic Behavioral Analysis](https://mlsanity.com/paper/2406.12438/) (2024)
- [SoK: Timeline based event reconstruction for digital forensics: Terminology, methodology, and current challenges](https://mlsanity.com/paper/2504.18131/) (2025)
- [Time-based GNSS attack detection](https://mlsanity.com/paper/2502.03868/) (2025)

## Built on by (most cited first, 1 of 1)

- [Time, Causality, and Observability Failures in Distributed AI Inference Systems](https://mlsanity.com/paper/2604.21361/) (2026)

## Abstract (arXiv)

Time integrity across distributed Internet of Things (IoT) devices is fundamental to reliable sensing, control, and security in energy cyber-physical systems. However, operational energy IoT systems remain vulnerable to clock-drift escalation, time-synchronization manipulation, and catastrophic timestamp discontinuities, e.g., the Year 2038 (Y2K38) Unix epoch overflow. These failures violate timestamp monotonicity, distort temporal ordering, and introduce structured inconsistencies in system observations. Conventional anomaly detection models, which typically assume reliable and uniformly ordered timestamps, are therefore ill-equipped to capture timing-layer failures. This paper introduces STGAT (Spatio-Temporal Graph Attention Network), a clock-dynamics-aware anomaly detection solution that jointly models temporal distortion and inter-device consistency in energy IoT systems. STGAT integrates drift-aware temporal embeddings and temporal self-attention to capture non-uniform and corrupted time evolution within individual device streams, while graph attention models the spatial propagation of timing inconsistencies across interconnected nodes. A Jacobian-regularized latent representation further promotes geometric separation between nominal clock evolution and anomalous temporal deformation caused by drift escalation, synchronization offsets, jitter accumulation, and epoch-overflow events. Experimental evaluation on energy IoT telemetry augmented with controlled timing-layer perturbations shows that STGAT achieves 95.7% accuracy, 94.0% precision, 92.0% recall, 93.0% F1-score, and 0.97 AUC under the primary test setting. STGAT also reduces detection delay to 2.3 time steps, corresponding to a 26% improvement over the closest baseline, while maintaining stable performance under overflow-induced discontinuities, stealthy drift escalation, and temporally induced physical inconsistencies.