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Yuzhong Chen1,2, Zhenyu Liu1, Yulin Liu3

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This study introduces improved attack graph modeling for network security. New algorithms enhance visualization and analysis of cyberattack strategies, improving detection and response efficiency.

Keywords:
attack graphattack modelgraph segmentationprocess mining

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Area of Science:

  • Cybersecurity
  • Computer Science
  • Data Mining

Background:

  • Attack graph modeling aids network security by analyzing intrusion alerts to understand attacker strategies.
  • Current methods lack a high-level view, hindering comprehensive analysis of complex cyber threats.
  • Manual analysis of detailed attack graphs is challenging and time-consuming for security administrators.

Purpose of the Study:

  • To develop advanced algorithms for generating more interpretable and scalable attack graphs.
  • To address the limitations of existing methods in providing a global perspective on attack strategies.
  • To enhance the efficiency and accuracy of cyberattack modeling for network security.

Main Methods:

  • Employed a heuristic process mining algorithm to generate initial attack graphs from alert data.
  • Developed a graph segmentation algorithm to simplify complex attack graphs into manageable subgraphs.
  • Proposed distributed algorithms using Hadoop MapReduce and Spark GraphX for handling large-scale alert data.

Main Results:

  • The heuristic process mining algorithm generates complete but complex initial attack graphs.
  • The proposed graph segmentation algorithm effectively simplifies complex graphs while preserving structure.
  • Distributed algorithms demonstrate significant improvements in accuracy and efficiency for large datasets.
  • Experimental validation confirms superior performance over comparative algorithms.

Conclusions:

  • The developed algorithms offer a more effective approach to attack graph modeling in network security.
  • Improved interpretability and scalability facilitate better understanding and response to cyber threats.
  • The distributed approach is crucial for analyzing massive volumes of security alert data efficiently.