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Article:Proactive Password Strength Analyzer Using Filters and Machine Learning Techniques

by Suganya G, Karpgavalli S, Christina V
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 7 - Number 14
Year of Publication: 2010
Authors: Suganya G, Karpgavalli S, Christina V
10.5120/1333-1788

Suganya G, Karpgavalli S, Christina V . Article:Proactive Password Strength Analyzer Using Filters and Machine Learning Techniques. International Journal of Computer Applications. 7, 14 ( October 2010), 1-5. DOI=10.5120/1333-1788

@article{ 10.5120/1333-1788,
author = { Suganya G, Karpgavalli S, Christina V },
title = { Article:Proactive Password Strength Analyzer Using Filters and Machine Learning Techniques },
journal = { International Journal of Computer Applications },
issue_date = { October 2010 },
volume = { 7 },
number = { 14 },
month = { October },
year = { 2010 },
issn = { 0975-8887 },
pages = { 1-5 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume7/number14/1333-1788/ },
doi = { 10.5120/1333-1788 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T19:56:15.492750+05:30
%A Suganya G
%A Karpgavalli S
%A Christina V
%T Article:Proactive Password Strength Analyzer Using Filters and Machine Learning Techniques
%J International Journal of Computer Applications
%@ 0975-8887
%V 7
%N 14
%P 1-5
%D 2010
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Passwords are ubiquitous authentication methods and they represent the identity of an individual for a system. Users are consistently told that a strong password is essential these days to protect private data. Despite the existence of more secure methods of authenticating users, including smart cards and biometrics, password authentication continues to be the most common means in use. Thus it is important for organizations to recognize the vulnerabilities to which passwords are subjected, and develop strong policies governing the creation and use of passwords to ensure that those vulnerabilities are not exploited. This work proposes a framework to analyze the strength of the password proactively. To analyze the chosen password, filters and support vector machine are employed. This framework can be implemented as a submodule of the access control scheme.

References
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  3. Giancarlo Ruffo, Francesco Bergadano, “EnFilter : A Password Enforcement and Filter Tool Based on Pattern Recognition Techniques”, Springer Berlin / Heidelberg, 1611-3349 (Online), Volume 3617/2005.
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  6. John Shawe-Taylor, Nello Cristianini, “Support Vector Machines and other kernel-based learning methods”, 2000, Cambridge University Press, UK.
  7. Vapnik V.N,”Statistical Learning Theory”, J.Wiley & Sons, Inc., 1998, New York.
  8. Soman K.P, Loganathan R, Ajay V, “ Machine Learning with SVM and other Kernel Methods”, 2009, PHI, India.
Index Terms

Computer Science
Information Sciences

Keywords

authentication proactive password strength filters support vector machine

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