{"id":13285,"date":"2021-12-08T21:03:57","date_gmt":"2021-12-09T02:03:57","guid":{"rendered":"https:\/\/carleton.ca\/scs\/?page_id=13285"},"modified":"2021-12-08T21:04:04","modified_gmt":"2021-12-09T02:04:04","slug":"tr-08-21-on-predicting-and-exploiting-hot-spots-in-click-based-graphical-passwords","status":"publish","type":"page","link":"https:\/\/carleton.ca\/scs\/research\/scs-technical-reports\/technical-reports-2008\/tr-08-21-on-predicting-and-exploiting-hot-spots-in-click-based-graphical-passwords\/","title":{"rendered":"TR-08-21: On Predicting and Exploiting Hot-Spots in Click-Based Graphical Passwords"},"content":{"rendered":"

Carleton University
\nTechnical Report<\/a> TR-08-21
\nNovember 7, 2008<\/p>\n

On Predicting and Exploiting Hot-Spots in Click-Based Graphical Passwords<\/h2>\n
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P.C. van Oorschot & J. Thorpe<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n

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Abstract<\/h3>\n

We provide an in-depth study of the security of click-based graphical password schemes like PassPoints (Weidenbeck et al., 2005), by exploring popular points (hot-spots), and examining strategies to predict and exploit them in guessing attacks. We report on both short- and long-term user studies: one lab-controlled, involving 43 users and 17 diverse images, the other a field test of 223 user accounts. We provide empirical evidence that hot-spots do exist for many images, some more so than others. We explore the use of “human-computation” (in this context, harvesting click-points from a small set of users) to predict these hot-spots. We generate two “human-seeded” attacks based on this method: one based on a first-order Markov model, another based on an independent probability model. Within 100 guesses, our first-order Markov model-based attack guesses 4% of passwords in one instance, and 10% of passwords in a second instance. Our independent model-based attack guesses 20% within 2^33 guesses in one instance and 36% within 2^31 guesses in a second instance. These are all for a system whose full password space has cardinality 2^43. We also evaluate our first-order Markov model-based attack with cross-validation of the field study data, finding that it guesses an average of 7-10% of user passwords within 3 guesses. Our results suggest that these graphical password schemes (as originally proposed) are vulnerable to offline and online attacks, even on systems that implement conservative lock-out policies.<\/p>\n

TR-08-21.pdf<\/a><\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

Carleton University Technical Report TR-08-21 November 7, 2008 On Predicting and Exploiting Hot-Spots in Click-Based Graphical Passwords P.C. van Oorschot & J. Thorpe Abstract We provide an in-depth study of the security of click-based graphical password schemes like PassPoints (Weidenbeck et al., 2005), by exploring popular points (hot-spots), and examining strategies to predict and exploit […]<\/p>\n","protected":false},"author":49,"featured_media":0,"parent":12410,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_relevanssi_hide_post":"","_relevanssi_hide_content":"","_relevanssi_pin_for_all":"","_relevanssi_pin_keywords":"","_relevanssi_unpin_keywords":"","_relevanssi_related_keywords":"","_relevanssi_related_include_ids":"","_relevanssi_related_exclude_ids":"","_relevanssi_related_no_append":"","_relevanssi_related_not_related":"","_relevanssi_related_posts":"","_relevanssi_noindex_reason":"","_mi_skip_tracking":false,"_exactmetrics_sitenote_active":false,"_exactmetrics_sitenote_note":"","_exactmetrics_sitenote_category":0,"footnotes":"","_links_to":"","_links_to_target":""},"yoast_head":"\nTR-08-21: On Predicting and Exploiting Hot-Spots in Click-Based Graphical Passwords - 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