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The BeiHang Keystroke Dynamics Authentication System  [PDF]
Juan Liu,Baochang Zhang,Linlin Shen,Jianzhuang Liu,Jason Zhao
Computer Science , 2013,
Abstract: Keystroke Dynamics is an important biometric solution for person authentication. Based upon keystroke dynamics, this paper designs an embedded password protection device, develops an online system, collects two public databases for promoting the research on keystroke authentication, exploits the Gabor filter bank to characterize keystroke dynamics, and provides benchmark results of three popular classification algorithms, one-class support vector machine, Gaussian classifier, and nearest neighbour classifier.
Keystroke Dynamics Authentication For Collaborative Systems  [PDF]
Romain Giot,Mohamad El-Abed,Christophe Rosenberger
Computer Science , 2009, DOI: 10.1109/CTS.2009.5067478
Abstract: We present in this paper a study on the ability and the benefits of using a keystroke dynamics authentication method for collaborative systems. Authentication is a challenging issue in order to guarantee the security of use of collaborative systems during the access control step. Many solutions exist in the state of the art such as the use of one time passwords or smart-cards. We focus in this paper on biometric based solutions that do not necessitate any additional sensor. Keystroke dynamics is an interesting solution as it uses only the keyboard and is invisible for users. Many methods have been published in this field. We make a comparative study of many of them considering the operational constraints of use for collaborative systems.
A Survey of Biometric keystroke Dynamics: Approaches, Security and Challenges  [cached]
Mrs. D. Shanmugapriya,Dr. G. Padmavathi
International Journal of Computer Science and Information Security , 2009,
Abstract: Biometrics technologies are gaining popularity today since they provide more reliable and efficient means of authentication and verification. Keystroke Dynamics is one of the famous biometric technologies, which will try to identify the authenticity of a user when the user is working via a keyboard. The authentication process is done by observing the change in the typing pattern of the user. A comprehensive survey of the existing keystroke dynamics methods, metrics, different approaches are given in this study. This paper also discusses about the various security issues and challenges faced by keystroke dynamics..Keywords- Biometris; Keystroke Dynamics; computer Security; Information Security; User Authentication.
Keystroke Dynamics User Authentication Based on Gaussian Mixture Model and Deep Belief Nets  [PDF]
Yunbin Deng,Yu Zhong
ISRN Signal Processing , 2013, DOI: 10.1155/2013/565183
Abstract: User authentication using keystroke dynamics offers many advances in the domain of cyber security, including no extra hardware cost, continuous monitoring, and nonintrusiveness. Many algorithms have been proposed in the literature. Here, we introduce two new algorithms to the domain: the Gaussian mixture model with the universal background model (GMM-UBM) and the deep belief nets (DBN). Unlike most existing approaches, which only use genuine users’ data at training time, these two generative model-based approaches leverage data from background users to enhance the model’s discriminative capability without seeing the imposter’s data at training time. These two new algorithms make no assumption about the underlying probability distribution and are fast for training and testing. They can also be extended to free text use cases. Evaluations on the CMU keystroke dynamics benchmark dataset show over 58% reduction in the equal error rate over the best published approaches. 1. Introduction With the ever increasing demand for more secure access control in many of today’s security applications, traditional methods fail to keep up with the challenges because pins, tokens, and passwords are too many to remember. Even carefully crafted user name and password can be hacked, which compromises the system security. On the other hand, biometrics [1–5] based on “who” the person is or “how” the person acts, as compared with what the person has (key) and knows (password), presents a significant security advancement to meet these new challenges. Among them, keystroke dynamics [6] provides a natural choice for secure “password-free” computer access with no additional hardware required. Keystroke dynamics refers to the habitual patterns or rhythms an individual exhibits while typing on a keyboard input device. These rhythms and patterns of tapping are idiosyncratic, [7] the same way as handwritings or signatures are, due to their similar governing neurophysiological mechanisms. Back in the 19th century, telegraph operators could recognize each other based on one’s specific tapping style [8]. Recently, it is shown that typing text can be deciphered simply based on the sound of key typing [9]. As such, it is believed that the keystroke dynamics contains enough information to be a good biometrics to ascertain a user at the keyboard. Compared with other biometrics, keystroke biometrics has additional attractiveness for its user-friendliness and nonintrusiveness. Keystroke dynamics data can be collected without a user’s awareness. Continuous authentication is possible using
Keystroke Dynamics Based Authentication Using Possibilistic Renyi Entropy Features and Composite Fuzzy Classifier  [PDF]
Aparna Bhatia, Madasu Hanmandlu
Journal of Modern Physics (JMP) , 2018, DOI: 10.4236/jmp.2018.92008
Abstract: This paper presents the formulation of the possibilistic Renyi entropy function from the Renyi entropy function using the framework of Hanman-Anirban entropy function. The new entropy function is used to derive the information set features from keystroke dynamics for the authentication of users. A new composite fuzzy classifier is also proposed based on Mamta-Hanman entropy function and applied on the Information Set based features. A comparison of the results of the proposed approach with those of Support Vector Machine and Random Forest classifier shows that the new classifier outperforms the other two.
Keystroke Authentication on Enhanced Needleman Alignment Algorithm  [PDF]
Seham Bamatraf, Mohamed Bamatraf, Osman Hegazy
Intelligent Information Management (IIM) , 2014, DOI: 10.4236/iim.2014.64021
Abstract:

An important point for computer systems is the identification of users for authentication. One of these identification methods is keystroke dynamics. The keystroke dynamics is a biometric measurement in terms of keystroke press duration and keystroke latency. However, several problems are arisen like the similarity between users and identification accuracy. In this paper, we propose innovative model that can help to solve the problem of similar user by classifying user’s data based on a membership function. Next, we employ sequence alignment as a way of pattern discovery from the user’s typing behaviour. Experiments were conducted to evaluate accuracy of the proposed model. The results show high performance compared to standard classifiers in terms of accuracy and precision.

A Survey of Biometric keystroke Dynamics: Approaches, Security and Challenges  [PDF]
D. Shanmugapriya,G. Padmavathi
Computer Science , 2009,
Abstract: Biometrics technologies are gaining popularity today since they provide more reliable and efficient means of authentication and verification. Keystroke Dynamics is one of the famous biometric technologies, which will try to identify the authenticity of a user when the user is working via a keyboard. The authentication process is done by observing the change in the typing pattern of the user. A comprehensive survey of the existing keystroke dynamics methods, metrics, different approaches are given in this study. This paper also discusses about the various security issues and challenges faced by keystroke dynamics.
FEATURE FUSION APPROACH ON KEYSTROKE DYNAMICS EFFICIENCY ENHANCEMENT
Pin Shen Teh,Shigang Yue,Andrew B.J. Teoh
International Journal of Cyber-Security and Digital Forensics , 2012,
Abstract: In this paper we study the performance and effect of diverse keystroke feature combinations on keystroke dynamics authentication system by using fusion approach. First of all, four types of keystroke features are acquired from our collected dataset, later then transformed into similarity scores by using Gaussian Probability Density Function (GPD) and Direction Similarity Measure (DSM). Next, three fusion approaches are introduced to merge the scores pairing with different combinations of fusion rules. Result shows that the finest performance is obtained by the combination of both dwell time and flight time collectively. Finally, this experiment also investigates the effect of using larger dataset on recognition performance, which turns out to be rather consistent.
An Introduction to Information Sets with an Application to Iris Based Authentication  [PDF]
Madasu Hanmandlu, Mamta Bansal, Shantaram Vasikarla
Journal of Modern Physics (JMP) , 2020, DOI: 10.4236/jmp.2020.111008
Abstract: This paper presents the information set which originates from a fuzzy set on applying the Hanman-Anirban entropy function to represent the uncertainty. Each element of the information set is called the information value which is a product of the information source value and its membership function value. The Hanman filter that modifies the information set is derived by using a filtering function. Adaptive Hanman-Anirban entropy is formulated and its properties are given. It paves the way for higher form of information sets called Hanman transforms that evaluate the information source based on the information obtained on it. Based on the information set six features, Effective Gaussian Information source value (EGI), Total Effective Gaussian Information (TEGI), Energy Feature (EF), Sigmoid Feature (SF), Hanman transform (HT) and Hanman Filter (HF) features are derived. The performance of the new features is evaluated on CASIA-IRIS-V3-Lamp database using both Inner Product Classifier (IPC) and Support Vector Machine (SVM). To tackle the problem of partially occluded eyes, majority voting method is applied on the iris strips and this enables better performance than that obtained when only a single iris strip is used.
INVESTIGATING & IMPROVING THE RELIABILITY AND REPEATABILITY OF KEYSTROKE DYNAMICS TIMERS  [PDF]
Pavaday Narainsamy,Soyjaudah Sunjiv,Nugessur Shrikaant
International Journal of Network Security & Its Applications , 2010,
Abstract: One of the most challenging tasks facing the security expert remains the correct authentication of humanbeing which has been crucial to the fabric of our society. The emphasis is now on reliable personidentification for computerized devices as the latter forms an integral part of our daily activities.Moreover with increasing geographical mobility of individuals, the identification problem has becomemore acute. One alternative, to curb down the increasing number of computer related crimes, is throughthe use of keystroke biometric technology which represents an enhancement to password mechanisms byincorporating typing rhythms in it.Time captured being critical to the performance of the identifier, it is primordial that it satisfies certainrequirements at a suitable degree of acceptability This paper presents an evaluation of timing options forkeystroke dynamics paying attention to their repeatability and reliability as well as their portability ondifferent systems. In actual passwords schemes users enroll using one computer and access resourcesusing other configurations at different locations without bothering about the different underlyingoperating systems.
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