Measuring the revised guessability of graphical passwords

There is no widely accepted way of measuring the level of security of a recognition-based graphical password against guessing attacks. We aim to address this by examining the influence of predictability of user choice on the guessability and proposing a new measure of guessability. Davis et al. showed that these biases exist for schemes using faces and stories, we support this result and show these biases exist in other recognition-based schemes. In addition, we construct an attack exploiting predictability, which we term “Semantic Ordered Guessing Attack” (SOGA). We then apply this attack to two schemes (the Doodles scheme and a standard recognition-based scheme using photographic images) and report the results. The results show that predictability when users select graphical passwords influence the level of security to a varying degree (dependent on the distractor selection algorithm). The standard passimages scheme show an increase on guessability of up to 18 times more likely than the usual reported guessability, with a similar set up of nine images per screen and four screens, the doodles scheme shows a successful guessing attack is 3.3 times more likely than a random guess. Finally, we present a method of calculating a more accurate guessability value, which we call the revised guessability of a recognition-based scheme. Our conclusion is that to maximise the security of a recognition-based graphical password scheme, we recommend disallowing user choice of images.