We address the problem of shoulder-surfing attacks on authentication schemes by proposing IllusionPIN (IPIN), a PIN-based authentication method that operates on touchscreen devices. IPIN uses the technique of hybrid images to blend two keypads with different digit orderings in such a way, that the user who is close to the device is seeing one keypad to enter her PIN, while the attacker who is looking at the device from a bigger distance is seeing only the other keypad. The user’s keypad is shuffled in every authentication attempt, since the attacker may memorize the spatial arrangement of the pressed digits. To reason about the security of IPIN, we developed an algorithm which is based on human visual perception and estimates the minimum distance from which an observer is unable to interpret the keypad of the user. We tested our estimations with 84 simulated shoulder-surfing attacks from 21 different people. None of the attacks was successful against our estimations. In addition, we estimated the minimum distance from which a camera is unable to capture the visual information from the keypad of the user. Based on our analysis, it seems practically almost impossible for a surveillance camera to capture the PIN of a smartphone user when IPIN is in use.
The principle states that it has to be difficult to receive the visual information of interest @cite_44 . For example, DAS @cite_15 is a simple graphical password that allows the user to create a free-form drawing on a touchscreen and to use it as her password. Decoy Strokes @cite_35 is a shoulder-surfing resistant variation of DAS that draws strokes alongside the user's password to confuse a malicious observer. The problem with such schemes is that the user is exposed to the same distracting information and may end up confused as well, leading to slower authentication and more frequent input errors. Also, if the attacker is able to observe multiple times during the authentication process or to record it, he may be able to steal the credentials of the user.
@cite_44 @cite_35 @cite_15
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Draw a secret (DAS) is a representative graphical password scheme. Rigorous theoretical analysis suggests that DAS supports an overall password space larger than that of the ubiquitous textual password scheme. However, recent research suggests that DAS users tend to choose weak passwords, and their choices would render this theoretically sound scheme less secure in real life. In this paper we investigate the novel idea of introducing background images to the DAS scheme, where users were initially supposed to draw passwords on a blank canvas overlaid with a grid. Encouraging results from our two user studies have shown that people aided with background images tended to set significantly more complicated passwords than their counterparts using the original scheme. The background images also reduced other predictable characteristics in DAS passwords such as symmetry and centering within the drawing grid, further improving the strength of the passwords. We estimate that the average strength of successfully recalled passwords in the enhanced scheme was increased over those created using the original scheme by more than 10 bits. Moreover, a positive effect was observed with respect to the memorability of the more complex passwords encouraged by the background images. Alphanumeric passwords are widely used in computer and network authentication to protect users' privacy. However, it is well known that long, text-based passwords are hard for people to remember, while shorter ones are susceptible to attack. Graphical password is a promising solution to this problem. Draw-A-Secret (DAS) is a typical implementation based on the user drawing on a grid canvas. Currently, too many constraints result in reduction in user experience and prevent its popularity. A novel graphical password strategy Yet Another Graphical Password (YAGP) inspired by DAS is proposed in this paper. The proposal has the advantages of free drawing positions, strong shoulder surfing resistance and large password space. Experiments illustrate the effectiveness of YAGP. Traditional password based authentication scheme is vulnerable to shoulder surfing attack. So if an attacker sees a legitimate user to enter password then it is possible for the attacker to use that credentials later to illegally login into the system and may do some malicious activities. Many methodologies exist to prevent such attack. These methods are either partially observable or fully observable to the attacker. In this paper we have focused on detection of shoulder surfing attack rather than prevention. We have introduced the concept of tag digit to create a trap known as honeypot. Using the proposed methodology if the shoulder surfers try to login using others’ credentials then there is a high chance that they will be caught red handed. Comparative analysis shows that unlike the existing preventive schemes, the proposed methodology does not require much computation from users end. Thus from security and usability perspective the proposed scheme is quite robust and powerful. Shoulder-surfing -- using direct observation techniques, such as looking over someone's shoulder, to get passwords, PINs and other sensitive personal information -- is a problem that has been difficult to overcome. When a user enters information using a keyboard, mouse, touch screen or any traditional input device, a malicious observer may be able to acquire the user's password credentials. We present EyePassword, a system that mitigates the issues of shoulder surfing via a novel approach to user input. With EyePassword, a user enters sensitive input (password, PIN, etc.) by selecting from an on-screen keyboard using only the orientation of their pupils (i.e. the position of their gaze on screen), making eavesdropping by a malicious observer largely impractical. We present a number of design choices and discuss their effect on usability and security. We conducted user studies to evaluate the speed, accuracy and user acceptance of our approach. Our results demonstrate that gaze-based password entry requires marginal additional time over using a keyboard, error rates are similar to those of using a keyboard and subjects preferred the gaze-based password entry approach over traditional methods.