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Updated: Sep 9, 2025

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
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A robust and dynamic malware detection and classification model using behavioral-based analysis and BERT technique.

Abdulrahman Hassan Alhazmi1

  • 1Department of Computer Science, College of Engineering and Computer Science, Jazan University, Jazan, Kingdom of Saudi Arabia.

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|September 4, 2025
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Summary
This summary is machine-generated.

This study introduces a behavior-based malware classification model using BERT for feature extraction, achieving 92.25% accuracy. Support Vector Machines and Random Forest demonstrated strong performance in identifying malware families.

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Area of Science:

  • Cybersecurity
  • Machine Learning
  • Software Engineering

Background:

  • Malware classification is difficult due to evolving threats.
  • Signature-based and static analysis methods are insufficient for sophisticated malware.
  • Behavior-based analysis is critical for effective malware detection.

Purpose of the Study:

  • To propose a novel malware detection model analyzing executable file behavior.
  • To enhance malware classification accuracy using BERT for feature extraction.
  • To evaluate the performance of different machine learning classifiers on malware families.

Main Methods:

  • Executable files (.exe) were analyzed for behavior in a secure environment via VirusTotal.
  • BERT model was employed to extract features from behavior logs.
  • Support Vector Machines (SVM), Random Forest, and Naïve Bayes classifiers were evaluated.

Main Results:

  • The proposed behavior-based model achieved 92.25% accuracy and 91.22% F1-score after 100 epochs.
  • SVM and Random Forest showed high F1-scores for Adware (0.98) and BackDoor (0.91).
  • Naïve Bayes performed poorly for FakeAlert (F1-score: 0.64).

Conclusions:

  • Behavior-based analysis combined with BERT features is effective for malware classification.
  • SVM and Random Forest are reliable classifiers for this task.
  • Understanding inter-class relationships through correlation analysis is valuable.