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Related Experiment Video

Updated: Aug 6, 2025

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
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Real-time botnet detection on large network bandwidths using machine learning.

Javier Velasco-Mata1,2, Víctor González-Castro3,4, Eduardo Fidalgo3,4

  • 1Department of Electrical Systems and Automation Engineering, Universidad de León, 24071, León, Spain. javier.velasco@unileon.es.

Scientific Reports
|March 16, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces an ultra-fast botnet detection method for analyzing network traffic in real-time. The approach achieves high accuracy (0.926 F1-score) with minimal processing time, even on high-bandwidth networks.

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Area of Science:

  • Computer Science
  • Cybersecurity
  • Network Security

Background:

  • Botnets pose significant threats, causing substantial economic losses globally.
  • Manual analysis of vast network traffic is infeasible.
  • Effective botnet detection requires high-speed processing, especially on large bandwidths.

Purpose of the Study:

  • To develop an ultra-fast network analysis approach for botnet detection.
  • To maintain high detection accuracy (F1-score) with rapid processing.
  • To evaluate the model's performance on saturated and high-bandwidth networks.

Main Methods:

  • Proposed an approach for ultra-fast network traffic analysis within one-second windows.
  • Compared the model's performance against three existing literature proposals.
  • Assessed model robustness on networks with packet loss and varying bandwidths.

Main Results:

  • Achieved the best performance with an F1-score of 0.926 and a processing time of 0.007 ms per sample.
  • Demonstrated robustness on networks with up to 10% packet loss.
  • Estimated CPU core requirements for different bandwidths (e.g., 4 cores for 1 Gbps, 19 cores for 10 Gbps).

Conclusions:

  • The proposed approach enables effective botnet detection at high speeds without significant accuracy loss.
  • The model is robust and scalable for various network conditions, including high saturation and bandwidth.
  • Efficient resource utilization is achievable for real-time botnet detection on modern networks.