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Malware detection based on semi-supervised learning with malware visualization.

Tan Gao1, Lan Zhao2, Xudong Li1

  • 1School of Cyber Science and Engineering, Sichuan University, China.

Mathematical Biosciences and Engineering : MBE
|September 14, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a novel malware classification framework using malware visualization and semi-supervised learning. The approach effectively reduces labeling costs and enhances classification accuracy by utilizing unlabeled samples.

Keywords:
Malicious sample detectioncollaborative learningfeature fusionnoise robustness

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Area of Science:

  • Computer Science
  • Cybersecurity
  • Machine Learning

Background:

  • Traditional signature-based malware detection is resource-intensive and struggles with evolving threats.
  • Machine learning methods face challenges with extensive manual sample labeling, leading to unusable unlabeled data.

Purpose of the Study:

  • To propose an effective malware classification framework leveraging malware visualization and semi-supervised learning.
  • To address the limitations of traditional methods in terms of cost and adaptability to malware mutations.

Main Methods:

  • A framework integrating malware visualization, feature extraction from grayscale images, and feature fusion.
  • Implementation of an improved collaborative learning algorithm for semi-supervised classification using unlabeled samples.

Main Results:

  • The proposed framework effectively processes binary files directly into visual representations.
  • Feature fusion eliminates exclusion between different feature variables, enhancing analysis.
  • The improved collaborative learning algorithm demonstrated reduced sample labeling costs and improved model performance with unlabeled data.

Conclusions:

  • The developed framework offers a more efficient and accurate approach to malware classification.
  • Semi-supervised learning with malware visualization is a promising direction for combating rapidly evolving malware threats.