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A Fusion Model Based on Dynamic Web Browsing Behavior Analysis for IoT Insider Threat Detection.

Jiarong Wang1, Junyi Liu1, Tian Yan1

  • 1Institute of High Energy Physics, Chinese Academy of Sciences, 19B Yuquan Road, Shijingshan District, Beijing 100049, China.

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Detecting insider threats in enterprises is challenging with IoT devices. This study introduces a fusion model analyzing individual and peer-group web browsing behaviors for more accurate detection of abnormal users and insider threats.

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Area of Science:

  • Cybersecurity
  • Network Security
  • Behavioral Analytics

Background:

  • Traditional security measures struggle with insider threats in enterprise Internet of Things (IoT) environments.
  • Existing methods for detecting anomalous web browsing behavior overlook user dynamics and peer group consistency, leading to inefficiencies and low accuracy.

Purpose of the Study:

  • To develop a more accurate and efficient method for insider threat detection in enterprise networks.
  • To address the limitations of current anomaly detection techniques by incorporating dynamic user behavior and peer group analysis.

Main Methods:

  • Proposed an individual user behavior model to capture dynamic changes in browsing patterns.
  • Introduced a peer-group behavior model to analyze behavioral consistency within user groups.
  • Developed a fusion model combining individual and peer-group models for comprehensive insider threat detection.

Main Results:

  • The proposed fusion model effectively characterizes abnormal dynamic changes in user browsing behavior.
  • The model accurately identifies behavioral inconsistencies among peer groups.
  • Experimental results demonstrate the fusion model's capability in accurately detecting insider threats based on anomalous web browsing activities.

Conclusions:

  • The fusion model offers a significant improvement in insider threat detection accuracy for enterprise networks.
  • Analyzing both individual and peer-group browsing behaviors is crucial for robust security.
  • The approach provides a more effective solution for the evolving landscape of IoT security challenges.