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Related Experiment Video

Updated: Aug 25, 2025

Eye Movement Monitoring of Memory
08:06

Eye Movement Monitoring of Memory

Published on: August 15, 2010

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Memory Visualization-Based Malware Detection Technique.

Syed Shakir Hameed Shah1, Norziana Jamil1, Atta Ur Rehman Khan2

  • 1Institute of Energy Infrastructure, College of Computing and Informatics, Universiti Tenaga Nasional, Kajang 43000, Malaysia.

Sensors (Basel, Switzerland)
|October 14, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a novel data engineering approach for detecting polymorphic malware by denoising and re-dimensioning memory images. The new method significantly improves machine learning model performance and reduces computational costs.

Keywords:
advanced persistent threatcomputer visiondenoising filtersenergy securitymachine learningmalware analysismemory analysispolymorphic malwarewavelet transform

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Area of Science:

  • Cybersecurity
  • Machine Learning
  • Data Engineering

Background:

  • Advanced Persistent Threats (APTs) pose significant risks, especially when combined with polymorphic malware that evades detection.
  • Existing malware detection methods using memory visualization lack effective preprocessing, leading to overfitting and poor generalization.
  • Polymorphic malware's ability to generate variants and reside in main memory makes it particularly challenging to identify.

Purpose of the Study:

  • To develop a new data engineering approach for enhancing malware detection.
  • To address the overfitting and generalization issues in memory-based malware visualization techniques.
  • To improve the accuracy and efficiency of detecting polymorphic malware within Advanced Persistent Threats.

Main Methods:

  • Introduced a two-stage data engineering approach: Denoising and Re-Dimensioning.
  • Denoising stage: Reduced noise in memory dump-based malware images.
  • Re-Dimensioning stage: Compressed cleaned images to reduce dimensionality, mitigating overfitting and computational overhead.

Main Results:

  • Achieved high performance metrics: 97.82% accuracy, 97.66% precision, 97.25% recall, and 97.57% F1-score.
  • Outperformed existing solutions by 0.83% in accuracy, 0.30% in precision, 1.67% in recall, and 1.25% in F1-score.
  • Significantly reduced computational time and memory usage compared to prior methods.

Conclusions:

  • The proposed data engineering approach effectively enhances machine learning models for malware detection.
  • The Denoising and Re-Dimensioning stages are crucial for overcoming limitations in current memory-based visualization techniques.
  • This method offers a more accurate, efficient, and generalizable solution for identifying polymorphic malware in APTs.