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Password strength svm

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  • 1. Password Strength Analysisthrough Support Vector Machine” By Sunil Kumar R.M Lecturer Department of CSE RLJIT
  • 2. Overview Introduction. Password strength. Support vector machine. Feature extraction. Conclusion.
  • 3. Introduction passwords are used for many purposes  logging in to computer accounts.  retrieving e-mail from servers.  transferring funds.  shopping online. Passwords are more sophisticated service-granting systems, such as Kerberos. passwords are needed for protecting secret information that cannot be remembered by the user (e.g. private keys) in authentication and encryption software that is becoming essential to many applications.
  • 4. Contd.. there is a real and growing threat of data thieves, hackers and other criminals taking advantage of people who arent security conscious. organizations launch a multi-faceted defense against password breach. That begins with mandating that only secure passwords be used.
  • 5. Password strength. Password strength is a measurement of the effectiveness of a password . The key to a strong password is length and complexity. very weak, weak, moderate or medium or good, strong and very strong. at least eight characters, including a mixture of upper- and lower-case and some numbers and special characters
  • 6. Contd.. Most of the survey results reveal that 10% to 15% of the user’s passwords used mixed case, numbers, and symbols. Commercial tools available for password strength checking include Google Password Meter (Google, 2008), Microsoft password Checker (Microsoft, 2008) and The Password Meter (Password Meter, 2008)
  • 7. Support vector machine SVMs maximize the margin around the separating hyperplane.  A.k.a. large margin classifiers The decision function is fully specified by a subset of training samples, the support vectors. Solving SVMs is a quadratic programming problem Seen by many as the most successful current text classification method*
  • 8.  The system automatically identifies a subset of informative points called support vectors and uses them to represent the separating hyper plane which is sparsely a linear combination of these points. Finally SVM solves a simple convex optimization problem. two class pattern recognition problem, yi = +1 yi = -1. A training example (xi,yi) is called positive if yi = +1 and negative otherwise.
  • 9. FEATURE EXTRACTION Improving classification effectiveness, computational efficiency or both. passwords are generated using PC Tools Password Generator. A weight is assigned to each relevant feature. The twenty-seven descriptive features are created as a fixed length vector for password analysis. (Length of the password, Weight of the password, Number of lowercase characters, Lowercase character weight age, Number of uppercase characters, Uppercase character weight age, Number of digits, Digits weight age, Number of symbols, Weight age of symbols, Number of middle number and symbols)
  • 10. weighting method A weighting method is adopted for computing the strength of the password.
  • 11. PASSWORD CLASSIFICATION
  • 12. Conclusion This work describes the machine learning approach for determining the strength of the password. Support Vector Machine, a supervised pattern classification technique has been applied for training the password strength analysis model. Features are extracted from the set of 10,000 passwords of different categories to facilitate training and implementation.

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