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Related Experiment Video

Updated: Oct 8, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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Malicious Code Variant Identification Based on Multiscale Feature Fusion CNNs.

Shuo Wang1, Jian Wang1, Yafei Song1

  • 1College of Air and Missile Defense, Air Force Engineering University, Xi'an 710051, China.

Computational Intelligence and Neuroscience
|December 24, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a novel deep learning approach for malware classification. The multiscale feature fusion convolutional neural networks (MFFCs) model achieves high accuracy and rapid detection of malware families.

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Area of Science:

  • Computer Science
  • Cybersecurity
  • Artificial Intelligence

Background:

  • Malware poses a significant threat to network security due to its increasing volume and diversity.
  • Deep Convolutional Neural Networks (CNNs) are effective for malware binary detection, but existing methods suffer from poor feature extraction, low accuracy, and high detection times.
  • There is a need for improved malware classification techniques that can handle variants and evasive malware.

Purpose of the Study:

  • To propose a novel deep learning approach, Multiscale Feature Fusion Convolutional Neural Networks (MFFCs), for effective malware classification.
  • To enhance malware detection capabilities against variants and confusing malware types.
  • To improve feature extraction, classification accuracy, and reduce detection time in malware analysis.

Main Methods:

  • Malware code binaries are converted into grayscale images.
  • The MFFC model is utilized for image normalization and feature extraction.
  • Deep learning techniques are applied for identifying malware families based on visual representations.

Main Results:

  • The MFFC model achieved a high accuracy of 98.72% on the Malimg dataset.
  • The average detection time was significantly reduced to 5.34 milliseconds.
  • Comparative experiments demonstrated superior performance over existing advanced methods.

Conclusions:

  • The MFFC approach offers excellent feature extraction capabilities for malware analysis.
  • The proposed method provides higher accuracy and lower detection time compared to current techniques.
  • MFFCs effectively identify malware and detect variants, enhancing network security.