Scholarlink

Cargo hijacking poses critical risks to global supply chains, yet most existing anomaly detection approaches are validated on single, homogeneous datasets and rely on static spatial boundaries that fail to capture realistic behavioral deviations. This study evaluates an unsupervised Long Short-Term Memory (LSTM) Autoencoder for real-time vehicle hijacking detection across heterogeneous logistics environments. The model was trained on a unified dataset of approximately 5.9 million GNSS observations integrating three distinct mobility profiles, and evaluated against road-constrained directional attacks generated via the Open Source Routing Machine (OSRM). A sensitivity-oriented decision threshold was adopted to prioritize threat detection over conservative filtering. Across 30 independent runs, the proposed framework achieved a mean Accuracy of 0.97 and a Recall of 0.99 on the unified dataset, with a clear separation between normal reconstruction error (MAE approximately 0.018) and attack error (MAE approximately 0.062). Inference latency averaged 0.69 s with a memory footprint of approximately 2.1 GB, without requiring GPU acceleration. These results confirm that training on diverse, heterogeneous data stabilizes the detection boundary and reduces false alarms without sacrificing sensitivity, providing a scalable and lightweight blueprint for securing logistics networks using only standard trajectory data.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Evaluating Long Short-Term Memory Autoencoders for Vehicle Hijacking Detection on Unified Heterogeneous Datasets

  • 会议论文

  • 发表日期：

    2026年

查看全文
  • Edison Solorzano,
  • Jorge Parraga-Alava,
  • Enrique Dominguez

摘要

Cargo hijacking poses critical risks to global supply chains, yet most existing anomaly detection approaches are validated on single, homogeneous datasets and rely on static spatial boundaries that fail to capture realistic behavioral deviations. This study evaluates an unsupervised Long Short-Term Memory (LSTM) Autoencoder for real-time vehicle hijacking detection across heterogeneous logistics environments. The model was trained on a unified dataset of approximately 5.9 million GNSS observations integrating three distinct mobility profiles, and evaluated against road-constrained directional attacks generated via the Open Source Routing Machine (OSRM). A sensitivity-oriented decision threshold was adopted to prioritize threat detection over conservative filtering. Across 30 independent runs, the proposed framework achieved a mean Accuracy of 0.97 and a Recall of 0.99 on the unified dataset, with a clear separation between normal reconstruction error (MAE approximately 0.018) and attack error (MAE approximately 0.062). Inference latency averaged 0.69 s with a memory footprint of approximately 2.1 GB, without requiring GPU acceleration. These results confirm that training on diverse, heterogeneous data stabilizes the detection boundary and reduces false alarms without sacrificing sensitivity, providing a scalable and lightweight blueprint for securing logistics networks using only standard trajectory data.