Keystroke dynamics: Characteristics and opportunities

Crawford, Heather

doi:10.1109/pst.2010.5593258

Cited by 72 publications

(35 citation statements)

References 23 publications

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“…Usually, there is a trade off between FAR and FRR, so that when FAR increases, FRR tends to decrease and vice-versa. • EER: EER (Equal Error Rate) represents the value when both FAR and FRR are equal [9]. [8]).…”

Section: Keystroke Dynamicsmentioning

confidence: 99%

Negative Selection with High-Dimensional Support for Keystroke Dynamics

Pisani

Lorena

2012

2012 Brazilian Symposium on Neural Networks

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Computing and communication systems have been expanding and bringing a number of advancements to our way of life. However, this technological evolution has also contributed to the rise of the identity theft, mainly due to the advent of the digital identity. An alternative to overcome this problem is by the analysis of the user behavior, known as behavioral intrusion detection. Among the possible aspects to be analysed, this work focuses on the keystroke dynamics, which consists of recognizing users by their typing rhythm. This paper draws a comparison between some novelty detectors applied to keystroke dynamics: immune negative selection algorithms and auto-associative neural networks. Issues regarding the use of negative selection in high dimensional spaces are discussed and an alternative to deal with this problem is presented.

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Section: Keystroke Dynamicsmentioning

confidence: 99%

Negative Selection with High-Dimensional Support for Keystroke Dynamics

Pisani

Lorena

2012

2012 Brazilian Symposium on Neural Networks

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“…imposter) would be given access to the system as a genuine user [30] whereas FRR is the rate at which an authorized user would be rejected the access to the system considering him as imposter. FAR is calculated as the ratio of imposters granted access to the total number of imposter attempts while FRR is calculated as the ratio of genuine users denied access to the total number of genuine attempts.…”

Section: Description Of Databases Usedmentioning

confidence: 99%

Keystroke Dynamics Based Authentication Using Information Sets

Bhatia¹,

Hanmandlu²

2017

JMP

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This paper presents keystroke dynamics based authentication system using the information set concept. Two types of membership functions (MFs) are computed: one based on the timing features of all the samples and another based on the timing features of a single sample. These MFs lead to two types of information components (spatial and temporal) which are concatenated and modified to produce different feature types. Two Component Information Set (TCIS) is proposed for keystroke dynamics based user authentication. The keystroke features are converted into TCIS features which are then classified by SVM, Random Forest and proposed Convex Entropy Based Hanman Classifier. The TCIS features are capable of representing the spatial and temporal uncertainties. The performance of the proposed features is tested on CMU benchmark dataset in terms of error rates (FAR, FRR, EER) and accuracy of the features. In addition, the proposed features are also tested on Android Touch screen based Mobile Keystroke Dataset. The TCIS features improve the performance and give lower error rates and better accuracy than that of the existing features in literature.

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“…Another KD survey was conducted by H. Crawford [14] in 2010. The author reviewed a representative subset of concurrent research in KD and provided recommendations for future work.…”

Section: Meta-surveymentioning

confidence: 99%

Authentication and Identification Methods Used in Keystroke Biometric Systems

Ali

Tappert

Qiu

et al. 2015

2015 IEEE 17th International Conference on High Performance Computing and Communications, 2015 IEEE 7th International Symposium

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Keystroke dynamics authentication is not as widely used compared to other biometric systems. In recent years, keystroke dynamic authentication systems have gained interest because of low cost and integration with existing security systems. Many different methods have been proposed for data collection, feature representation, classification, and performance evaluation. The work presents a detailed survey of the most recent research in keystroke dynamic authentication. Research is evaluated by the conditions under which data was collected, classification algorithms used, and system performance. This work also identifies some shortcomings of the current research issues that need to be addressed for keystroke dynamics to mature. Some recommendations for future research are made, with the goal of improving keystroke dynamics system performance and robustness.

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Keystroke dynamics: Characteristics and opportunities

Cited by 72 publications

References 23 publications

Negative Selection with High-Dimensional Support for Keystroke Dynamics

Negative Selection with High-Dimensional Support for Keystroke Dynamics

Keystroke Dynamics Based Authentication Using Information Sets

Authentication and Identification Methods Used in Keystroke Biometric Systems

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