Recent Advances in User Authentication Using Keystroke Dynamics Biometrics
GCSR Volume 2
ISBN 978-618-81418-3-4 (print) – ISBN 978-618-81418-2-7 (e-book)
Yu Zhong and Yunbin Deng
Chapter 1 - A Survey on Keystroke Dynamics Biometrics: Approaches, Advances, and Evaluations
Yu Zhong and Yunbin Deng
(pages 1-22) DOI: 10.15579/gcsr.vol2.ch1
In this review paper we present a comprehensive survey of research efforts in the past couple of decades on keystroke dynamics biometrics. We review the literature in light of various feature extraction, feature matching and classification methods for keystroke dynamics. We also discuss recent trends in keystroke dynamics research, including its use in mobile environments, as a soft biometrics, and its fusion with other biometric modalities. We further address the evaluation of keystroke biometric systems, including traditional and new performance metrics, and list publicly available keystroke datasets for performance benchmarks to promote synergy in the research community.
Chapter 2 - Keystroke Dynamics User Authentication Using Advanced Machine Learning Methods
Yunbin Deng and Yu Zhong
(pages 23-40) DOI: 10.15579/gcsr.vol2.ch2
User authentication based on typing patterns offers many advantages in the domain of cyber security, including data acquisition without extra hardware requirement, continuous monitoring as the keys are typed, and non-intrusive operation with no interruptions to a user’s daily work. In this chapter, we adopt three popular voice biometrics algorithms to perform keystroke dynamics based user authentication, namely, 1) Gaussian Mixture Model with Universal Background Model (GMM-UBM), 2) identity vector (i-vector) approach to user modelling, and 3) deep machine learning approach. Unlike most existing keystroke biometrics approaches, which only use genuine users’ data at training time, the proposed methods leverage data from a large pool of background users to enhance the model’s discriminative capability. These algorithms make no assumption about the underlying probability distribution of the data and are amenable to real-time implementation. Although these techniques were originally developed for speech analysis, our experiments on the publicly available CMU keystroke dynamics dataset using these algorithms have shown significant reduction in the equal error rate over other published approaches. Finally, we discuss challenges and concerns for practical deployment of keystroke authentication technology.
Chapter 3 - Continuous Authentication with Keystroke Dynamics
Patrick Bours and Soumik Mondal
(pages 41-58) DOI: 10.15579/gcsr.vol2.ch3
In this chapter we will discuss how keystroke dynamics can be used for true continuous authentication. We have collected keystroke dynamics data of 53 participants who used the computer freely and we have analysed the collected data. We will describe a system that decides on the genuineness of the user based on each and every single keystroke action of the current user and we will represent the results in a new manner.
The continuous authentication system will lock out a user if the trust in genuineness of the current user is too low. Ideally such a system would never lock out a genuine user and detect an impostor user within as few keystroke actions as possible.
Chapter 4 - Keystroke Dynamics Advances for Mobile Devices Using Deep Neural Network
Yunbin Deng and Yu Zhong
(pages 59-70) DOI: 10.15579/gcsr.vol2.ch4
Recent popularity in mobile devices has raised concerns on mobile technology security, as not only sensitive and private data are being stored on mobile devices, but also allowing remote access to other high value assets. This drives research efforts to new mobile technology security methods. Fortunately, new mobile devices are equipped with advanced sensor suite, enabling a multi-modal biometrics authentication solution, to include voice, face, gait, signature, and keystroke authentication, among others. Compared with other modalities, keystroke authentication offer some very attractive features: 1) non-intrusive, either password or free-text typing keystroke authentication can be applied without affecting users’ daily user of the device; 2) it can work on continuous authentication mode for free typing; 3) it can leverage a unique set of advanced build in sensors, including accelerometer and gyroscope to capture rich typing information than raw timing pattern. We present a deep learning approach , which is a very powerful advanced machine leaning method, to the challenging problem of keystroke dynamics biometric. We further take advantage of the rich sensor modalities available for mobile devices and strengthen our keystroke dynamics biometrics using multi-modal typing features.
Chapter 5 - Will User Authentication Using Keystroke Dynamics Biometrics Be Interfered by Emotions? – NCTU-15 Affective Keyboard Typing Dataset for Hypothesis Testing
Po-Ming Lee, Liang-Yu Chen, Wei-Hsuan Tsui and Tzu-Chien Hsiao
(pages 71-81) DOI: 10.15579/gcsr.vol2.ch5
In this chapter we 1) provide a new dataset collected from real-world for researchers to examine possible influence of emotions on user authentication using keystroke dynamics biometrics, or develop their own systems to recognize emotions using keystroke dynamics patterns, 2) summarize recent findings in the field of emotion recognition using keystroke dynamics, and 3) provide concrete suggestions to the field of user authentication using keystroke dynamics biometrics based on the empirical findings derived from the proposed dataset.