Gaussian Mixture Modeling of Keystroke Patterns for Biometric Applications

Hosseinzadeh, Danoush; Krishnan, Sridhar

doi:10.1109/tsmcc.2008.2001696

Cited by 94 publications

(47 citation statements)

References 28 publications

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“…Both classical statistical methods [39,46] and advanced machine learning approaches have been used, including K-Nearest Neighbor (KNN) classifiers [21,112], K-means methods [59], Bayesian classifiers [81], Fuzzy logic [44], Boost learning [8], and Random Forests [8,76], etc. Support vector machine (SVM) is a powerful machine learning method which computes decision boundaries by maximizing the margin in order to reduce the generalization error.…”

Section: Keystroke Dynamics Classification Using Statistical and Advamentioning

confidence: 99%

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A Survey on Keystroke Dynamics Biometrics: Approaches, Advances, and Evaluations

Zhong¹,

Deng²

2015

Gate to Computer Science and Research

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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.

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Section: Keystroke Dynamics Classification Using Statistical and Advamentioning

confidence: 99%

“…Chen and Chang [20] and Jiang et al [56] have respectively used Hidden Markov Models to learn the non-deterministic temporal dynamics in typing rhythms. A Gaussian mixture model approach has been used in [46] as well.…”

Section: Keystroke Dynamics Classification Using Statistical and Advamentioning

confidence: 99%

A Survey on Keystroke Dynamics Biometrics: Approaches, Advances, and Evaluations

Zhong¹,

Deng²

2015

Gate to Computer Science and Research

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“…The long history of keystroke research has also resulted in other various approaches, including K-means methods [32], fuzzy logic [29], fuzzy ARTMAP, histogram equalization of time intervals, Gaussian mixture model (GMM) [33], hidden Markov model (HMM), and genetic algorithms.…”

Section: The Literature Surveymentioning

confidence: 99%

“…At testing time, a keystroke feature is evaluated against the genuine user's GMM, and a threshold is applied to the likelihood of the feature vector to make the decision [33]. The idea of GMM-UBM is to train another GMM from a large pool of the so-called background subjects (except for the genuine user and the testing subjects), in addition to a GMM for each genuine subject.…”

Section: Gmm-ubmmentioning

confidence: 99%

Keystroke Dynamics User Authentication Based on Gaussian Mixture Model and Deep Belief Nets

Deng¹,

Zhong²

2013

ISRN Signal Processing

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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.

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“…다른 생체 기반 정보의 경우, 2차 인증에 사용되기 위해 서는 추가적인 하드웨어의 설치가 필수적인 반면에, 키스트로 크 다이나믹스는 유일하게 소프트웨어만으로 개인의 생체 기 반 정보를 처리할 수 있는 장점으로 인하여 웹 기반 서비스의 대표적인 2차 인증 방법론으로 널리 연구되어 왔다 (Giot et al, 2010;Monrose et al, 2002;Monrose and Rubin, 2000;Peacock et al, 2004). (Chen and Chang, 2004;Hosseinzadeh and Krishnan, 2008;Sinthupinyo et al, 2009;Sheng et al, 2005;Zhang et al, 2010). 일반적으로 인증 알고리즘의 복잡도가 증가할수록 등록 단계에서 요구하는 정상적 사용자 의 키스트로크 다이나믹스 데이터 수집 횟수는 증가한다 (Kang et al, 2008 …”

Section: 서 론 정보통신 기술의 꾸준한 발전과 함께 언제 어디서나 접속이 가능한 유비쿼터스 네트워크(Ubiquitouunclassified

A Study on User Authentication based on Keystroke Dynamics of Long and Free Texts

Kang¹,

Cho²

2012

IE interfaces

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Keystroke dynamics refers to a way of typing a string of characters. Since one has his/her own typing behavior, one's keystroke dynamics can be used as a distinctive biometric feature for user authentication. In this paper, two authentication algorithms based on keystroke dynamics of long and free texts are proposed. The first is the K-S score, which is based on the Kolmogorov-Smirnov test, and the second is the 'R-A' measure, which combines 'R' and 'A' measures proposed by Gunetti and Picardi (2005). In order to verify the authentication performance of the proposed algorithms, we collected more than 3,000 key latencies from 34 subjects in Korean and 35 subjects in English. Compared with three benchmark algorithms, we found that the K-S score was outstanding when the reference and test key latencies were not sufficient, while the 'R-A' measure was the best when enough reference and test key latencies were provided. 이러한 패스워드 기반 사용자 인증 방법론의 약점을 극복하기 위하여 패스워드와 생체 기반 정보(biometrics)를 결합한 다양한 2차 인증 방법론들이 연구되었다 (Furnell and Clarke, 2005;Jain et al., 1999;Prabhakar et al., 2003;Yager and Dunstone, 2010). 생체 기반 정보는 그 형태에 따라서 지문, 홍채, 동공 등 개인이 유전 본 연구는 서울과학기술대학교 교내연구과제의 지원을 받아 수행되었습니다. †

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Gaussian Mixture Modeling of Keystroke Patterns for Biometric Applications

Cited by 94 publications

References 28 publications

A Survey on Keystroke Dynamics Biometrics: Approaches, Advances, and Evaluations

A Survey on Keystroke Dynamics Biometrics: Approaches, Advances, and Evaluations

Keystroke Dynamics User Authentication Based on Gaussian Mixture Model and Deep Belief Nets

A Study on User Authentication based on Keystroke Dynamics of Long and Free Texts

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