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 Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Neutralization Method of Ransomware Detection Technology Using Format Preserving Encryption.

Sensors (Basel, Switzerland)·2023
Same author

Effective Ransomware Detection Using Entropy Estimation of Files for Cloud Services.

Sensors (Basel, Switzerland)·2023
Same author

True Random Number Generator (TRNG) Utilizing FM Radio Signals for Mobile and Embedded Devices in Multi-Access Edge Computing.

Sensors (Basel, Switzerland)·2019
See all related articles

Related Experiment Video

Updated: Nov 27, 2025

Experimental Assessment of Mouse Sociability Using an Automated Image Processing Approach
08:24

Experimental Assessment of Mouse Sociability Using an Automated Image Processing Approach

Published on: May 15, 2016

8.8K

Improved Practical Vulnerability Analysis of Mouse Data According to Offensive Security based on Machine Learning in

Kyungroul Lee1, Sun-Young Lee2

  • 1R&BD Center for Security and Safety Industries (SSI), Soonchunhyang University, Asan-si, Chungnam 31538, Korea.

Entropy (Basel, Switzerland)
|December 8, 2020
PubMed
Summary

This study demonstrates a machine learning approach to enhance mouse data attack accuracy. By analyzing coordinate distances, researchers achieved over 99% accuracy in distinguishing user mouse data from defender-generated data.

Keywords:
machine learningoffensive securitypractical securityuser authenticationvulnerability analysis

More Related Videos

A Real-Time Interactive System for Studying Confrontational Pursuit Behavior in Rodents
06:25

A Real-Time Interactive System for Studying Confrontational Pursuit Behavior in Rodents

Published on: May 16, 2025

938
A System for Tracking the Dynamics of Social Preference Behavior in Small Rodents
08:38

A System for Tracking the Dynamics of Social Preference Behavior in Small Rodents

Published on: November 21, 2019

7.9K

Related Experiment Videos

Last Updated: Nov 27, 2025

Experimental Assessment of Mouse Sociability Using an Automated Image Processing Approach
08:24

Experimental Assessment of Mouse Sociability Using an Automated Image Processing Approach

Published on: May 15, 2016

8.8K
A Real-Time Interactive System for Studying Confrontational Pursuit Behavior in Rodents
06:25

A Real-Time Interactive System for Studying Confrontational Pursuit Behavior in Rodents

Published on: May 16, 2025

938
A System for Tracking the Dynamics of Social Preference Behavior in Small Rodents
08:38

A System for Tracking the Dynamics of Social Preference Behavior in Small Rodents

Published on: November 21, 2019

7.9K

Area of Science:

  • Computer Science
  • Cybersecurity
  • Machine Learning

Background:

  • Mouse dynamics, including coordinate data, can be exploited for user identification and potential security breaches.
  • Existing mouse data attack techniques lack sufficient accuracy for reliable user profiling.

Purpose of the Study:

  • To assess the feasibility of improving mouse data attack accuracy using machine learning.
  • To develop and validate a novel feature extraction method for mouse movement data.

Main Methods:

  • Machine learning models were employed to analyze features derived from user mouse coordinate data.
  • A specific feature, the distance concentration between coordinates, was identified and analyzed.
  • The proposed method was tested against defender-generated mouse data.

Main Results:

  • A key feature was identified where the distance between mouse coordinates concentrates within a specific range.
  • Utilizing this distance feature significantly improved the accuracy of mouse data attack techniques.
  • The developed method achieved an accuracy exceeding 99% in classifying user versus defender mouse data.

Conclusions:

  • Mouse coordinate distance is a viable and highly effective feature for enhancing mouse data attack accuracy.
  • The proposed machine learning-based method offers a robust solution for distinguishing genuine user mouse data.
  • This research highlights the significant security risks associated with unencrypted or unprotected mouse movement data.