[论文解读] Keystroke Dynamics: Concepts, Techniques, and Applications

Rashik Shadman, Ahmed Anu Wahab+4 more|arXiv (Cornell University)|Mar 8, 2023

User Authentication and Security Systems被引用 10

一句话总结

本论文综述用于身份认证的击键动态，涵盖数据类型、性能指标、数据集、算法和应用，包括连续认证。

ABSTRACT

Reliably identifying and verifying subjects remains integral to computer system security. Various novel authentication techniques, such as biometric authentication systems, have been developed in recent years. This paper provides a detailed review of keystroke-based authentication systems and their applications. Keystroke dynamics is a behavioral biometric that is emerging as an important tool for cybersecurity as it promises to be non-intrusive and cost-effective. In addition, no additional hardware is required, making it convenient to deploy. This survey covers novel keystroke datasets, state-of-the-art keystroke authentication algorithms, keystroke authentication on touch screen and mobile devices, and various prominent applications of such techniques beyond authentication. The paper covers all the significant aspects of keystroke dynamics and can be considered a reference for future researchers in this domain. The paper includes a discussion of the latest keystroke datasets, providing researchers with an up-to-date resource for analysis and experimentation. In addition, this survey covers the state-of-the-art algorithms adopted within this domain, offering insights into the cutting-edge techniques utilized for keystroke analysis. Moreover, this paper explains the diverse applications of keystroke dynamics, particularly focusing on security, verification, and identification uses. Furthermore, this paper presents a summary of future research opportunities, highlighting potential areas for exploration and development within the realm of keystroke dynamics. This forward-looking perspective aims to inspire further inquiry and innovation, guiding the trajectory of future studies in this dynamic field.

研究动机与目标

• Motivate keystroke dynamics as a cost-effective, non-intrusive behavioral biometric for authentication and continuous verification.
• Summarize the landscape of keystroke datasets, data collection practices, and data types (fixed, free, semi-fixed).
• Review performance metrics and evaluation methodologies used in keystroke-based systems.
• Compare existing surveys and highlight gaps and trends in recent keystroke dynamics research.

提出的方法

• Collect and synthesize keystroke dynamics research from 2013 to present from Google Scholar."
• Categorize prior work by contribution: datasets, algorithms, preprocessing techniques, devices (desktop/tablet/mobile), and applications.
• Describe and summarize performance metrics (FAR, FRR, EER, ROC/AUC) and additional metrics (ANIA, ANGA, authentication time).
• Present and compare keystroke datasets (e.g., CMU, GreyC, Pace, Clarkson, Buffalo, Aalto, AR, Multi-K) and their data characteristics (fixed/free/semi-fixed).
• Survey keystroke authentication algorithms with emphasis on statistical methods (e.g., Gaussian Mixture Models) and other approaches as described in the source.
• Discuss applications, challenges, and trends including continuous authentication and cross-device/language considerations.

实验结果

研究问题

• RQ1 What keystroke datasets exist, and what are their characteristics (data type, subjects, sample size)?
• RQ2 What authentication algorithms are used in keystroke dynamics, and how do they perform across datasets?
• RQ3 What performance metrics are used to evaluate keystroke systems, and what trade-offs do they imply?
• RQ4 How do factors like fixed vs free-text, devices, and cross-language impacts affect keystroke dynamics performance?
• RQ5 What are current applications and challenges of keystroke-based authentication, including continuous and cross-domain contexts?

主要发现

• A wide range of datasets exist, spanning fixed, free, and semi-fixed text, with varying numbers of subjects and keystroke samples.
• Common evaluation metrics include FAR, FRR, and EER, with ROC/AUC providing threshold-insensitive performance views; ANIA and ANGA are used for continuous authentication.
• Gaussian Mixture Models and other statistical methods are frequently employed, alongside diverse data processing and feature extraction techniques for keystroke timings.
• Recent large-scale datasets (e.g., Aalto, AR, Multi-K) and benchmarks show evolving trends in mobile, cross-keyboard, and cross-language keystroke performance.
• Studies highlight significant factors affecting performance, such as keyboard type, language, and text content, underscoring the need for diverse, realistic datasets and cross-device evaluation.
• The survey consolidates multiple surveys and foregrounds gaps, including up-to-date coverage of non-password text (free and semi-fixed) keystroke systems and cross-device considerations.