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Real-time Spread Burst Detection in Data Streaming.

Haibo Wang1, Dimitrios Melissourgos2, Chaoyi Ma1

  • 1University of Florida, Gainesville, FL, USA.

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|May 8, 2024
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Summary
This summary is machine-generated.

This study introduces real-time burst detection in data flow spread, a novel problem for cybersecurity and network analysis. The developed solution efficiently identifies "super spreaders" in data streams, improving upon existing methods.

Keywords:
Data StreamingReal-timeSpread Burst

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Area of Science:

  • Computer Science
  • Data Science
  • Network Engineering

Background:

  • Data streaming is crucial for network monitoring, e-commerce, and social networks.
  • Traditional burst detection focuses on flow size, not flow spread.
  • Estimating flow spread in real-time presents significant computational challenges.

Purpose of the Study:

  • Introduce and address the novel problem of real-time burst detection in data flow spread.
  • Develop an efficient and accurate solution for identifying bursts in flow spread.
  • Contribute to cybersecurity, network engineering, and internet trend identification.

Main Methods:

  • Propose a new real-time super spreader identifier.
  • Develop a novel sketch design for real-time spread estimation.
  • Optimize for minimizing spread estimation overhead while maintaining accuracy.

Main Results:

  • The proposed super spreader identifier outperforms state-of-the-art methods in accuracy and processing overhead.
  • The new sketch design for spread estimation surpasses existing techniques.
  • The solution enables efficient, real-time burst detection in flow spread.

Conclusions:

  • This paper presents the first efficient, real-time solution for spread burst detection.
  • The novel methods offer significant improvements over current approaches.
  • The findings have practical implications for internet data analysis and security.