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Utilization of the Internet in our everyday lives has made us vulnerable in terms of privacy and security of our data and systems. Therefore, there is a pressing need to protect our data and systems by improving authentication mechanisms, which are expected to be low cost, unobtrusive, and ideally ubiquitous in nature. Behavioral biometric modalities such as mouse dynamics (mouse behaviors on a graphical user interface (GUI)) and widget interactions (another modality closely related to mouse dynamics that also considers the target (widget) of a GUI interaction, such as links, buttons, and combo-boxes) can bolster the security of existing authentication systems because of their ability to distinguish individuals based on their unique features. As a result, it can be difficult for an imposter to impersonate these behavioral biometrics, making them suitable for authentication. In this article, we survey the literature on mouse dynamics and widget interactions dated from 1897 to 2023. We begin our survey with an account of the psychological perspectives on behavioral biometrics. We then analyze the literature along the following dimensions: tasks and experimental settings for data collection, taxonomy of raw attributes, feature extractions and mathematical definitions, publicly available datasets, algorithms (statistical, machine learning, and deep learning), data fusion, performance, and limitations. We end the paper with presenting challenges and promising research opportunities.
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This content will become publicly available on March 29, 2026
Behavioral Biometrics for Continuous Authentication: Exploring the Challenges and Opportunities
This paper explores behavioral biometrics, an emerging authentication method leveraging unique user behavior patterns for continuous security. This dynamic approach offers enhanced protection compared to traditional methods, yet significant challenges must be addressed. A key concern, examined herein, is accuracy; false positives and false negatives can undermine system effectiveness. User frustration arises from false positives, while false negatives create security vulnerabilities. The work emphasizes the need for careful system tuning and advanced machine learning to mitigate these errors. Data privacy and security are also paramount, given the sensitive, non-replaceable nature of the collected information. The paper highlights the importance of robust security measures, user transparency, and informed consent. Furthermore, it acknowledges that natural human behavioral variability, influenced by physical and environmental factors, can impact authentication accuracy, necessitating adaptive systems. In conclusion, addressing these technical and ethical challenges is crucial for realizing the full potential of behavioral biometrics.
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- Award ID(s):
- 1754054
- PAR ID:
- 10623605
- Publisher / Repository:
- The 2025 ADMI Symposium.
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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