Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Video

Updated: Sep 11, 2025

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

An Explainable Hybrid CNN-Transformer Architecture for Visual Malware Classification.

Mohammed Alshomrani¹, Aiiad Albeshri¹, Abdulaziz A Alsulami²

¹Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia.

Sensors (Basel, Switzerland)

|August 14, 2025

Summary

Related Concept Videos

Masking and Demasking Agents

Masking and Demasking Agents

EDTA titrations may necessitate masking and demasking agents to temporarily protect a particular metal ion in a mixture from the EDTA reaction. These agents facilitate the sequential analysis of the metal ions by forming stable complexes with some—but not all—metal ions during certain steps.
There are many masking agents, such as cyanide, fluoride, triethanolamine, thiourea, and 2,3-bis(sulfanyl)propan-1-ol (formerly 2,3-dimercapto-1-propanol), with the masking agent chosen based on...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Improving Tree-Based Lung Disease Classification from Chest X-Ray Images Using Deep Feature Representations.

Bioengineering (Basel, Switzerland)·2026

Same author

Toward a Hybrid Intrusion Detection Framework for IIoT Using a Large Language Model.

Sensors (Basel, Switzerland)·2026

Same author

Improving Classification Performance in Dendritic Neuron Models through Practical Initialization Strategies.

Sensors (Basel, Switzerland)·2024

Same author

Towards an Effective Service Allocation in Fog Computing.

Sensors (Basel, Switzerland)·2023

Same author

Computer Aided COVID-19 Diagnosis in Pandemic Era Using CNN in Chest X-ray Images.

Life (Basel, Switzerland)·2022

Same author

LidSonic V2.0: A LiDAR and Deep-Learning-Based Green Assistive Edge Device to Enhance Mobility for the Visually Impaired.

Sensors (Basel, Switzerland)·2022

Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026

Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026

Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026

Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026

Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026

Same journal

Three-Dimensional Modeling and Performance Analysis of Dynamic mmWave V2I Networks Based on Stochastic Geometry.

Sensors (Basel, Switzerland)·2026

See all related articles

This summary is machine-generated.

A new hybrid deep learning model combining ConvNeXt-Tiny and Swin Transformer achieves 94.04% accuracy for visual malware classification. This approach offers an effective and interpretable solution for detecting evolving malicious code.

Area of Science:

Cybersecurity
Computer Vision
Machine Learning

Background:

Traditional signature-based malware detection struggles with evolving threats.
Visual malware classification, converting binaries to images, offers a promising alternative.
Deep learning models like CNNs and Transformers show potential for image-based malware analysis.

Purpose of the Study:

To develop and evaluate a hybrid deep learning model for enhanced visual malware classification.
To combine the strengths of Convolutional Neural Networks (CNNs) and Transformers for improved pattern recognition.
To assess the model's performance, interpretability, and real-time applicability.

Main Methods:

A hybrid deep learning architecture integrating ConvNeXt-Tiny (CNN) and Swin Transformer.

Keywords:

Grad-CAM convnext cybersecurity deep learning explainable AI malware classification vision transformer

Related Experiment Videos

Last Updated: Sep 11, 2025

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

Training and validation on benchmark datasets: Malimg, MaleVis, VirusMNIST (61 classes).

Evaluation using additional datasets (Maldeb, Dumpware-10) and Gradient-weighted Class Activation Mapping (Grad-CAM) for interpretability.

Main Results:

The hybrid model achieved 94.04% validation accuracy, surpassing individual ConvNeXt-Tiny (92.45%) and Swin Transformer (90.44%) models.
Achieved high accuracy on extended datasets: 98% on Maldeb and 97% on Dumpware-10.
Grad-CAM visualization confirmed the complementary feature extraction of CNN and Transformer components.

Conclusions:

The hybrid deep learning approach provides a robust and interpretable method for visual malware classification.
The model demonstrates practical applicability through real-time deployment scenarios.
This research contributes to more effective and trustworthy automated malware detection systems.