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An Efficient Alert Aggregation Method Based on Conditional Rough Entropy and Knowledge Granularity.

Jiaxuan Sun1, Lize Gu1, Kaiyuan Chen1

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

Entropy (Basel, Switzerland)
|December 8, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces an alert aggregation scheme using conditional rough entropy and knowledge granularity to reduce redundant network security alerts. The method effectively processes data, enhancing efficiency for subsequent analysis.

Keywords:
alert aggregationattribute similarityconditional rough entropyknowledge granularity

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Area of Science:

  • Cybersecurity
  • Information Security
  • Data Analysis

Background:

  • Network security devices generate numerous logs and alerts, leading to information overload.
  • Repetitive and redundant alert data hinders effective security analysis and response.

Purpose of the Study:

  • To propose an alert aggregation scheme to address redundant information from network security devices.
  • To improve the efficiency of alert data processing for subsequent analysis.

Main Methods:

  • Utilizing conditional rough entropy and knowledge granularity to determine attribute weights for alerts.
  • Calculating alert similarity based on weighted attributes.
  • Employing a sliding time window to aggregate similar alerts exceeding a defined threshold.

Main Results:

  • The proposed scheme effectively reduces redundant alerts in network security data.
  • Demonstrated improvement in data processing efficiency.
  • Provided accurate and concise data for alert fusion and analysis.

Conclusions:

  • The alert aggregation scheme successfully mitigates alert redundancy.
  • The method enhances the overall efficiency of network security data processing.
  • This approach supports more effective alert fusion and analysis.