Fuzzy Methods for Voice-Based Person Authentication

Tran, Dat; Wagner, Michael; Lau, Yee Wah; Gen, Mitsuo

doi:10.1541/ieejeiss.124.1958

Cited by 9 publications

(3 citation statements)

References 11 publications

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“…When D>=3 and L>=14, we have a good password. D>=3 means that the password is at least 3 characters different from the base dictionary words, and L>=14 means that there are at least 14 10 possible password candidates to be tried to crack the password.…”

Section: Password Quality Indicatormentioning

confidence: 99%

“…Authentication is responsible for verifying that a person is really who he/she claims, and authorization is about assigning appropriate privileges to the person after the verification of his/her identity. There are 3 types of authentications [10, p 209]: (i) something the user knows, for example, password and PIN (personal identity number), (ii) something the user has, for example, physical keys, access cards, and smart cards etc., and (iii) something the user is -so called biometric authentication, such as voice recognition [14], fingerprints matching, and iris scanning etc. Password authentication is simple, accurate, and effective and will continue to be the working horse of information security.…”

Section: Introductionmentioning

confidence: 99%

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Password Entropy and Password Quality

Campbell

Tran

et al. 2010

2010 Fourth International Conference on Network and System Security

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Passwords are the first line of defense for many computerized systems. The quality of these passwords decides the security strength of these systems. Many studies advocate using password entropy as an indicator for password quality where lower entropy suggests a weaker or less secure password. However, a closer examination of this literature shows that password entropy is very loosely defined. In this paper, we first discuss the calculation of password entropy and explain why it is an inadequate indicator of password quality. We then establish a password quality assessment scheme: password quality indicator (PQI). The PQI of a password is a pair ) , ( L D = λ , where D is the Levenshtein's editing distance of the password in relation to a dictionary of words and common mnemonics, and L is the effective password length. Finally, we propose to use PQI to prescribe the characteristics of good quality passwords.

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Section: Password Quality Indicatormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Password Entropy and Password Quality

Campbell

Tran

et al. 2010

2010 Fourth International Conference on Network and System Security

Self Cite

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“…In the Spot [15] software, the seeded region growing (SRG) algorithm has been employed [16]. SRG method is perhaps the most powerful one with respect to shape identification, but it relies on a seed growing methodology as well as the selection of the initial seeds [17].…”

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confidence: 99%

A novel neural network approach to cDNA microarray image segmentation

Wang

Zineddin

Liang

et al. 2013

Computer Methods and Programs in Biomedicine

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Microarray technology has become a great source of information for biologists to understand the workings of DNA which is one of the most complex codes in nature. Microarray images typically contain several thousands of small spots, each of which represents a different gene in the experiment. One of the key steps in extracting information from a microarray image is the segmentation whose aim is to identify which pixels within an image represent which gene. This task is greatly complicated by noise within the image and a wide degree of variation in the values of the pixels belonging to a typical spot. In the past there have been many methods proposed for the segmentation of microarray image. In this paper, a new method utilizing a series of artificial neural networks, which are based on multi-layer perceptron (MLP) and Kohonen networks, is proposed. The proposed method is applied to a set of real-world cDNA images. Quantitative comparisons between the proposed method and commercial software GenePix are carried out in terms of the peak signal-to-noise ratio (PSNR). This method is shown to not only deliver results comparable and even superior to existing techniques but also have a faster run time.

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