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Hybrid rule-based botnet detection approach using machine learning for analysing DNS traffic.

Saif Al-Mashhadi1,2, Mohammed Anbar1, Iznan Hasbullah1

  • 1National Advanced IPv6 Centre, Universiti Sains Malaysia, Penang, Malaysia.

Peerj. Computer Science
|August 30, 2021
PubMed
Summary

This study introduces a novel hybrid detection model for identifying Domain Name System (DNS)-based botnets. The approach analyzes anomalous DNS traffic, achieving high accuracy in detecting these cyber threats.

Keywords:
Botnet detectionDNS analysisMachine learningNetwork securityRule-based technique

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Area of Science:

  • Cybersecurity
  • Network Security
  • Machine Learning

Background:

  • Botnets pose significant threats by controlling millions of devices for cyber-attacks.
  • Domain Name System (DNS) is exploited by botmasters using Domain Generation Algorithms (DGA) and fast-flux techniques.
  • Anomalous DNS traffic is a key indicator of DNS-based botnet activity.

Purpose of the Study:

  • To examine DNS traffic abnormalities throughout the botnet lifecycle.
  • To extract significant features for improved botnet detection.
  • To propose a novel hybrid rule detection model.

Main Methods:

  • Analysis of anomalous DNS traffic during the botnet lifecycle.
  • Extraction and analysis of significant enriched features.
  • Application of two machine learning algorithms and their combined output for detection.

Main Results:

  • The proposed hybrid model achieved 99.96% accuracy.
  • The model demonstrated a low false-positive rate of 1.6%.
  • Outperformed existing state-of-the-art DNS-based botnet detection methods.

Conclusions:

  • The novel hybrid rule detection model effectively identifies DNS-based botnets.
  • The approach offers a significant improvement over current detection methods.
  • Accurate detection of botnets is crucial for Internet security.