Visible to the public Selection of Android Graphic Pattern Passwords

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Selection of Android Graphic Pattern Passwords

Lablet partners study mobile passwords, human behavior

Android mobile phones come with four embedded access methods: a finger swipe, a pattern, a PIN, or a password, in ascending order of security. In the pattern method, the user is required to select a pattern by traversing a grid of 3x3 points. A pattern must contain at least four points, cannot use a point twice, and all points along a path must be sequential and connected, that is, no skipping of points. The pattern can be visible or cloaked. When a user enables such a feature, how do they trade off security with usability? And, are there visual cues that lead users to select one password over another and whether for usability or security? Researchers at the U.S. Naval Academy and Swarthmore, partners of the University of Maryland Science of Security Lablet, conducted a large user study of preferences for usability and security of the Android password pattern to provide insights into user perceptions that inform choice.

The study by Adam Aviv and Dane Fichter, “Understanding Visual Perceptions of Usability and Security of Android’s Graphical Password Pattern,” used a survey methodology that asked participants to select between pairs of patterns indicating either a security or usability preference. By carefully selecting password pairs to isolate a visual feature, visual perceptions of usability and security of different features were measured. They had a sample size of 384 users, self selected on Amazon Mechanical Turk. They found visual features that can be attributed to complexity indicated a stronger perception of security, while spatial features, such as shifts up/down or left/right were not strong indicators either for security or usability.

In their study, Aviv and Fichter selected pairs of patterns based on six visual features: Length (total number of contacts points used), Crosses (occur when the pattern doubles-back on itself by tracing over a previously contacted point), Non-Adjacent (The total number of non-adjacent swipes which occur when the pattern double-backs on itself by tracing over a previously contacted point), Knight-Moves (two spaces in one direction and then one space over in another direction), Height (amount the pattern is shifted towards the upper or lower contact points), and Side (amount the pattern is shifted towards the left or right contact points).

They asked users to select between two passwords, indicating a preference for one password in the pair that met a particular criterion, such as perceived security or usability, compared to the other password. By carefully selecting these password pairs, visual features of the passwords can be isolated and the impact of that feature on users’ perception of security and usability measured.

The researchers concluded that spatial features have little impact, but more visually striking features have a stronger impact, with the length of the pattern being the strongest indicator of preference. These results were extended and applied by constructing a predictive model with a broader set of features from related work, and the researchers found that the distance factor, the total length of all the lines in a pattern, is the strongest predictor of preference. These findings provide insight into users’ visual calculus when assessing a password, and the information may be used to develop new password systems or user selection tools, like password meters.

Moreover, Aviv and Fichter concluded that, with a good predictive model of user preference, their findings could be applied to a broader set of passwords, including those not used in the survey, and that this research could be expanded.  For example, ranking data based on learned pairwise preferences is an active research area in machine learning, and the resulting rankings metric over all potential patterns in the space would be greatly beneficial to the community. It could enable new password selection procedures where users are helped in identifying a preferred usable password that also meets a security requirement. 

The study is available at:

Dr. Adam Aviv

Adam J. Aviv, Assistant Professor, Computer Science, USNA--PI

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(ID#: 15-5937)


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