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

Bogazici mouse dynamics dataset.

Arjen Aykan Kılıç1, Metehan Yıldırım2, Emin Anarım2

  • 1Boğaziçi University Computer Engineering.

Data in Brief
|May 27, 2021
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

Bogazici university smartphone accelerometer sensor dataset.

Data in brief·2022
Same author

Boğaziçi University distributed denial of service dataset.

Data in brief·2020
See all related articles

This study introduces a comprehensive mouse dynamics dataset for enhanced security. It enables robust testing of intrusion detection and authentication systems against various threats.

Area of Science:

  • Computer Science
  • Cybersecurity
  • Data Science

Background:

  • Session-based authentication and intrusion detection are critical in privileged access management.
  • The increasing volume of big data necessitates advanced security solutions.
  • A significant gap exists in publicly available, extensive mouse dynamics datasets.

Purpose of the Study:

  • To introduce a novel, extensive dataset of free-usage mouse dynamics data.
  • To provide a resource for developing and testing advanced security protocols.
  • To facilitate research in insider threat detection, remote unauthorized access, and physical access security.

Main Methods:

  • Developed a Python application to capture mouse movements, clicks, timestamps, and window information.
Keywords:
AuthenticationBehavioral biometricsInsider threatMouse dynamicsRemote unauthorised accessVerification

Related Experiment Videos

  • Collected 2550 hours of active usage data from 24 unique users.
  • Structured the dataset with training, internal attack test, and external attack test data for each user.
  • Main Results:

    • The dataset comprises seven variables and data from 24 users.
    • Five users with minimal data were designated as external users for threat detection testing.
    • Data is segmented into 10 days of frequent usage for training and remaining days for internal attack testing.

    Conclusions:

    • The released dataset is highly suitable for evaluating security procedures against insider threats, remote unauthorized access, and physical access.
    • This resource supports the advancement of session-based authentication and intrusion detection systems.
    • The availability of this dataset will accelerate research and development in cybersecurity.