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Anomaly Behavior Detection Based on Deep Learning in an IoT Environment.

Anqi Fu1, Jian Li1

  • 1School of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing 100876, China.

Sensors (Basel, Switzerland)
|December 31, 2025
PubMed
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This study introduces a novel method for detecting abnormal behaviors in video surveillance streams using temporal structural attention and contrastive learning. The approach significantly improves the accuracy and reliability of anomaly detection in smart cities and public security systems.

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Video surveillance is crucial for smart cities and public security.
  • Existing methods struggle with limited abnormal data, similar normal/abnormal segments, and poor temporal modeling.
  • Internet of Things (IoT) generates massive video streams requiring efficient anomaly detection.

Purpose of the Study:

  • To develop an advanced weakly supervised video anomaly detection method.
  • To address limitations of existing methods in handling data scarcity and temporal dependencies.
  • To enhance the efficiency and reliability of anomaly detection in IoT environments.

Main Methods:

  • Integration of temporal structural attention with contrastive learning.
  • Utilizing causal masks and temporal decay weights to constrain temporal relations and prevent future information leakage.
Keywords:
contrastive learningintelligent sensingtemporal structural attentionvideo anomaly detection

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  • Employing positive/negative offsets and contrastive learning to improve discriminability of abnormal segments.

    • Main Results:

    • Superior performance on public video anomaly detection datasets.
    • Achieved 98.1% AUC, 96.1% ACC, and 94.5% F1-score.
    • Demonstrated significant improvements over existing mainstream models.

    Conclusions:

    • The proposed method offers intelligent, efficient, and reliable anomaly detection for IoT video surveillance.
    • Significant implications for enhancing public safety and intelligent monitoring.
    • Effectively overcomes challenges in weakly supervised video anomaly detection.