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Updated: Nov 6, 2025

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Cyber-attack method and perpetrator prediction using machine learning algorithms.

Abdulkadir Bilen1, Ahmet Bedri Özer2

  • 1Criminal Department, General Directorate of Security, Ankara, Turkey.

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|May 6, 2021
PubMed
Summary
This summary is machine-generated.

This study uses machine learning to predict cyber-attack methods and identify perpetrators. Findings show Support Vector Machine Linear is best for attack detection, while Logistic Regression aids attacker identification.

Keywords:
Artificial intelligenceCrime predictionCyber attack-crimesData analysisMachine learningSecurity and privacy

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Area of Science:

  • Cybersecurity and Artificial Intelligence
  • Machine Learning Applications in Forensics

Background:

  • Cyber-attacks pose significant global financial and criminal threats.
  • Existing crime prediction models lack specificity for cyber-crime and attack methods.
  • Identifying cyber-attack perpetrators and methods is crucial for effective crime fighting.

Purpose of the Study:

  • To analyze cyber-crimes using two machine learning models.
  • To predict the effectiveness of features in detecting cyber-attack methods and perpetrators.
  • To assess the correlation between victim characteristics and cyber-attack probability.

Main Methods:

  • Utilized eight distinct machine learning algorithms for analysis.
  • Developed two models to predict cyber-attack methods and identify perpetrators.
  • Analyzed data including crime type, perpetrator gender, damage, and attack methods.

Main Results:

  • Support Vector Machine Linear achieved 95.02% accuracy in detecting cyber-attack methods.
  • Logistic Regression demonstrated 65.42% accuracy in identifying attackers.
  • Cyber-attack probability decreases with increased victim education and income levels.

Conclusions:

  • Machine learning models can effectively predict cyber-attack types and aid in perpetrator identification.
  • The proposed models can enhance the efficiency of cyber-crime units.
  • Findings suggest a correlation between victim socio-economic factors and cyber-attack risk.