Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

QR code security: an adaptive retraining approach for dynamic URL-based threat detection.

Hissah Almousa1, Suliman A Alsuhibany2

  • 1Department of Computer Science, College of Computer, Qassim University, Buraydah, 51452, Saudi Arabia. 441211996@qu.edu.sa.

Scientific Reports
|April 22, 2026
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Integrating semantic and content features into bloom filters for efficient image retrieval.

Scientific reports·2025
Same author

Transfer learning driven fake news detection and classification using large language models.

Scientific reports·2025
Same author

Innovative QR Code System for Tamper-Proof Generation and Fraud-Resistant Verification.

Sensors (Basel, Switzerland)·2025
Same author

An efficient algorithm for data transmission certainty in IIoT sensing network: A priority-based approach.

PloS one·2024
Same author

An intelligent algorithm for energy efficiency optimization in software-defined wireless sensor networks for 5G communications.

PloS one·2024
Same author

Performance Analysis of Bloom Filter for Big Data Analytics.

Computational intelligence and neuroscience·2023
Same journal

Turbulent flow in a vortex separator with a directed pipe inlet.

Scientific reports·2026
Same journal

Systematic characteristic evaluation of clay-based cementitious material derived from calcium carbide residue and waste tile powder.

Scientific reports·2026
Same journal

Retraction Note: Improvement of a rapid diagnostic application of monoclonal antibodies against avian influenza H7 subtype virus using Europium nanoparticles.

Scientific reports·2026
Same journal

Applying large language models to spam detection in the Kazakh low-resource language setting.

Scientific reports·2026
Same journal

An open-source 3D printing system enabling in-situ freeze-thaw processing of hydrogels.

Scientific reports·2026
Same journal

An enhanced EfficientNet framework for automated waste classification using cosine annealing and label smoothing.

Scientific reports·2026
See all related articles

This study introduces a BERT-based model to detect malicious URLs within QR codes, enhancing cybersecurity. The adaptive model continuously learns from new data, outperforming traditional methods for robust QR code security.

Area of Science:

  • Cybersecurity
  • Machine Learning
  • Information Security

Background:

  • QR codes are widely used for information exchange but are vulnerable to cyber threats.
  • Malicious QR codes can embed harmful content, including dangerous URLs, posing risks to users.
  • Traditional security measures like blacklists are insufficient against evolving cyber threats.

Purpose of the Study:

  • To develop a robust system for detecting malicious URLs embedded in QR codes.
  • To leverage pre-trained language modelling, specifically a BERT-based architecture, for enhanced QR code security.
  • To implement an adaptive model that refines its detection capabilities over time.

Main Methods:

  • Utilized a BERT-based architecture for classifying QR code URLs as benign or malicious.
Keywords:
Adaptive retrainingAttention mechanismBERT-based classificationCybersecurityMalicious URL detectionQR code

Related Experiment Videos

  • Employed periodic model refinement through continuous retraining with diverse, newly acquired URL data.
  • Focused on differentiating between legitimate and harmful URLs encoded within QR codes.

    • Main Results:

    • The proposed BERT model demonstrated superior accuracy in detecting malicious URLs.
    • The adaptive approach proved effective in keeping pace with evolving cyber threats.
    • Experimental findings indicate the model outperforms existing detection methods.

    Conclusions:

    • BERT-based models offer a powerful solution for securing QR codes against malicious URL threats.
    • Continuous model adaptation is crucial for maintaining effective cybersecurity against dynamic threats.
    • The developed system provides a significant advancement in safeguarding users from QR code-based attacks.