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Presented by: Payas Gupta

Multiple Password Interference in text Passwords and click based G raphical Passwords by Sonia Chiasson , Alian Forget, Elizabeth Stobert , PC van Oorschot and Robert Biddle. Presented by: Payas Gupta. Motivation.

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Presented by: Payas Gupta

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  1. Multiple Password Interference in text Passwords and click based Graphical PasswordsbySonia Chiasson, Alian Forget, Elizabeth Stobert, PC van Oorschot and Robert Biddle Presented by: Payas Gupta

  2. Motivation • We know that people generally have difficulty remembering multiple passwords. • To compare multiple text password recalls with recall of multiple click-based graphical password. • Short term • Long term

  3. What it is about? • No algorithm no technique • It has only user study. • But a message as how to show such results in a nice way

  4. PassPoints • 5 click points in the same order • Tolerance accepted around each click point

  5. Hotspots • Dictionary attacks in graphical password: • Areas of the image that have higher probability of being selected by users.

  6. Study Details • Hypothesis • Click based graphical passwords would be easier for users to recall than text passwords when users had multiple passwords to remember. • Less interference from multiple unique graphical passwords than multiple unique text passwords.

  7. Specific hypothesis • Participants will have lower recall success rates with text passwords than with PassPoints passwords. • Participants in the Text condition are more likely than PassPointsparticipants to use patterns across their own passwords. • Participants will recall text passwords more slowly than PassPointspasswords. • Participants in the Text condition are more likely than Pass-Points participants to create passwords that are directly related to their corresponding accounts. • Participants in the Text condition will make more recall errors than participants in the PassPoints condition.

  8. Demographics • 65 participants • 26 males and 39 females • Participants were primarily university students from various degree programs. • None were expert in computer security

  9. Methodology • 65 participants in session 1 • Second session after two weeks • 26 participants

  10. Session 1 • Create • Confirm • Answer Questions • Perceived difficulty of creating • Perform Distraction Task • Mental rotation test • Login • Retry as many times to get it correct

  11. Results • Used chi-square test to compare non-ordered categorical data (comparing login/failure ratios). • Success rate • The success rate is the number of successful password entry attempts divided by the total number of attempts, across all participants.

  12. Recall 1 • First attempt • Text passwords – 68% • PassPoints – 95% • Participants could try recalling their password as many times as they wished, until they either succeeded or gave up. • Participants in the Text condition reached an 88% success rate with multiple recall attempts, compared to 99% for PassPoints participants.

  13. Recall 2 • Two weeks after creating their passwords, only 70% of Text participants and 57% of PassPointsparticipants were able to successfully recall their passwords. • Higher accuracies in male in passpoints. • Result aligns with psychology research • Male tend to perform better in visual and female in linguistic tasks

  14. Recall Errors

  15. Success rate for male and female • Recall 2

  16. Timings • Recall-1 • Participants were quicker at entering PassPoints passwords and this aligns with the fact that participants made fewer errors in the passpoints condition (when participants repeatedly entered the passwords). • Recall-2 • No significant difference

  17. Use of Mnemonics • 23 out of 34 (68%) participants in the Text condition used the account as a cue for at least one of their passwords. • Some passwords were directly linked with the account name. • instantmsgfor the instant messenger • “lovelove” for the online dating account • 40% of text passwords were related to their account • males being more likely to create passwords that were directly related to their accounts

  18. For text conditions • Recall 1 • Participants classified as having used account-related text passwords had a 96% success rate for Recall-1 while those who did not had an 83% recall success rate. • Recall 2 • Those classified as having created account-related passwords had a 71% success rate for Recall-2, while those who did not had a 69% success rate.

  19. Text Password Patterns • 71 out of 204 passwords (35%) were obviously related to other passwords created by the same user • ins901333” for the instant messenger account and “lib901333” for the library account

  20. PassPoints Patterns • The earlier study found that in PassPoints, participants were likely to select click-points in simple patterns such as a straight line or C- shape

  21. Comparison PPLab and MPP

  22. Found no statistical difference between the patterns found in the current study (where participants had to create and remember multiple passwords) and the earlier PassPoints lab study (where participants had to remember only one password at a time). • Two participants had 4 out of 6 passwords following a “Z” pattern

  23. Text Password Dictionary Attack • First tested passwords using the free dictionary of 4 million entries. • Followed by a second attack using a larger dictionary of 40 million entries purchased from the John the Ripper web site. • Smaller cracked 9.8% • Larger cracked 15.2%

  24. Examples of passwords that were not cracked by John the Ripper include: “msnhotmail” for an email password, “instantmsg” for an instant messenger account, and “inlibrary” for a library account. • In an earlier study of text passwords [16], 9.5% (18 out of 190) of passwords were cracked using John the Ripper with the same 4 million entry dictionary and 18.9% (36 out of 190) of passwords with the larger dictionary.

  25. Passpoints hotspot formation • To evaluate PassPoints passwords for predictability, we compared the distribution of click-points in the current study to those of an earlier PassPoints study on the same images [6]. • Wanted to see whether there was increased clustering of click-points across participants.

  26. The J-function measures the level of clustering of points within a dataset. • 32 PassPoints participants for each image in this study (160 click-points per image). • The earlier PassPointsdatasets [6] contained between 155 to 220 click-points per image.

  27. J-stat

  28. Validation of hypothesis • Participants will have lower recall success rates with text passwords than with PassPoints passwords. • Hypothesis partially supported. • Participants in the Text condition are more likely than PassPointsparticipants to use patterns across their passwords. • Hypothesis partially supported. • Participants will recall text passwords more slowly than PassPointspasswords. • Hypothesis partially supported.

  29. Participants in the Text condition are more likely than PassPointsparticipants to create passwords that are directly related to their corresponding accounts. • Hypothesis supported. • Participants in the Text condition will make more recall errors than participants in the PassPointscondition. • Hypothesis supported.

  30. Not a mirror image of real life • Unlikely to create 6 passwords one at a time • No one in our study wrote down their password, users often tend to do so. • However, examining the issue of multiple password interference in a controlled laboratory setting is an important step in understanding the effects of increased memory load and the coping behaviours exhibited by users.

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