An application of fuzzy algorithms in a computer access security system

Hussien, Bassam; McLaren, Robert W.; Bleha, S.A.

doi:10.1016/0167-8655(89)90026-3

Cited by 29 publications

(7 citation statements)

References 3 publications

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“…Some neural network approaches [1,3,12] have also been undertaken in the last few years. While the back-propagation models used yield favorable performance results on small databases, neural networks have a fundamental limitation in that each time a new user is introduced into the database, the network must be retrained.…”

Section: The Current State Of Keystroke Dynamicsmentioning

confidence: 99%

Keystroke dynamics as a biometric for authentication

Monrose

Rubin

2000

Future Generation Computer Systems

576

301

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More than ever before the Internet is changing computing as we know it. Global access to information and resources is becoming an integral part of nearly every aspect of our lives. Unfortunately, with this global network access comes increased chances of malicious attack and intrusion. In an effort to confront the new threats unveiled by the networking revolution of the past few years reliable, rapid, and unintrusive means for automatically recognizing the identity of individuals are now being sought. In this paper we examine an emerging non-static biometric technique that aims to identify users based on analyzing habitual rhythm patterns in the way they type.

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Section: The Current State Of Keystroke Dynamicsmentioning

confidence: 99%

Keystroke dynamics as a biometric for authentication

Monrose

Rubin

2000

Future Generation Computer Systems

576

301

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“…password code is important). 2 User identification number and typed password were checked (Preventive security and detection of violations ! both password and user identification codes are important).…”

Section: Both Time Intervals and Lpcs Used à Casementioning

confidence: 99%

“…The systems were evaluated using measurements of the time duration between successive keystrokes that appeared while a password was being entered via a keyboard. It is mentioned in [2] that a fuzzy perceptron algorithm gave good results in classifying data of valid users. Depending on the fuzzier value and the number of vectors used in a training set, a value of 8% as the highest error for the valid users has been obtained.…”

Section: Introductionmentioning

confidence: 98%

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Designing A Neural Network Based Computer Access Security System: Keystroke Dynamics and/or Voice Patterns

Anagün

2002

International Journal of Smart Engineering System Design

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A neural network architecture is proposed to deal with a situation of multiple users where each user has his=her own password with a different length. Two groups of experiments are conducted to design a reliable computer access security system. The neural networks are trained using time intervals between successive keystrokes during password entry through keyboard and voice patterns spoken via a microphone, respectively. The performance of the neural networks designed for each of the experiments is evaluated in terms of recognition accuracy. Two major issues, preventive security and detection of violations, which may be in question for a security system, are examined. A two-stage procedure is proposed to make a security system more reliable during on-line entry.

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“…From traditional distance measurement methods to neural network techniques, algorithms employed in related keystroke studies are quite different. For example, Garcia [23] uses Mahalanobis distance function in their study; Young and Hammon [24] adopt Euclidean distance; neural Some network approaches have also been undertaken in [25], [32], [33] and [16]. Perceptron algorithm is used by Bleha and Obaidat in [26].…”

Section: Classification Methodsmentioning

confidence: 99%

User habitation in keystroke dynamics based authentication

Cheng¹

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Most computer systems use usernames and passwords for authentication and access control. For long, password security has been framed as a tradeoff between user experience and password security. Trading off one for the other appears to be an inevitable dilemma for single password based security applications. As a new biometric for authenticating access, keystroke dynamics offers great promises in hardening the password mechanism. Our research first investigate the keystroke dynamics based password security by conducting an incremental study on user's habituation process for keystroke dynamics analysis using two distinct types of passwords. The study shows that 1) long and complex passwords are more efficient to be employed in keystroke dynamics systems; and 2) there is a habituation and acclimation process before the user obtains a stable keystroke pattern and the system collects enough training data. Then, based on our findings, we propose a two passwords mechanism that attempts to strike the right balance over user experience and password security by adopting a conventional easy-to-memorize password followed by a long-and-complex phrase for keystroke dynamics verification. Analysis and experimental studies successfully demonstrate the effectiveness of our proposed approach.

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An application of fuzzy algorithms in a computer access security system

Cited by 29 publications

References 3 publications

Keystroke dynamics as a biometric for authentication

Keystroke dynamics as a biometric for authentication

Designing A Neural Network Based Computer Access Security System: Keystroke Dynamics and/or Voice Patterns

User habitation in keystroke dynamics based authentication

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