Smartphones store a significant amount of personal and private information, and are playing an increasingly important role in people's lives. It is important for authentication techniques to be more resistant against two known attacks called shoulder surfing and smudge attacks. In this work, we propose a new technique called 3D Pattern. Our 3D Pattern technique takes advantage of a new input paradigm called pre-touch, which could soon allow smartphones to sense a user's finger position at some distance from the screen. We implement the technique and evaluate it in a pilot study (n=6) by comparing it to PIN and pattern locks. Our results show that although our prototype takes about 8 seconds to authenticate, it is immune to smudge attacks and promises to be more resistant to shoulder surfing.
Several works have examined the possibility of augmenting PIN keypads with gestures. SwiPIN, by von @cite_6 , divided the PIN keypad into two sections. Each number in each section corresponded to a different swipe gesture direction. Performing a swipe gesture on the correct section of the screen would insert the corresponding number. Their study demonstrated that this technique improved resistance against smudge attacks. introduced ForcePINs'' @cite_11 , with which each PIN digit could be entered with different levels of finger pressure on the screen, to add an additional layer of challenge for shoulder surfers. However, results showed that there was no statistically significant difference in shoulder-surfing resistance between regular PINs and ForcePINs, because when users pressed harder, they also pressed for a noticeably longer time.
@cite_6 @cite_11
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With the rich functionalities and enhanced computing capabilities available on mobile computing devices with touch screens, users not only store sensitive information (such as credit card numbers) but also use privacy sensitive applications (such as online banking) on these devices, which make them hot targets for hackers and thieves. To protect private information, such devices typically lock themselves after a few minutes of inactivity and prompt a password PIN pattern screen when reactivated. Passwords PINs patterns based schemes are inherently vulnerable to shoulder surfing attacks and smudge attacks. Furthermore, passwords PINs patterns are inconvenient for users to enter frequently. In this paper, we propose GEAT, a gesture based user authentication scheme for the secure unlocking of touch screen devices. Unlike existing authentication schemes for touch screen devices, which use what user inputs as the authentication secret, GEAT authenticates users mainly based on how they input, using distinguishing features such as finger velocity, device acceleration, and stroke time. Even if attackers see what gesture a user performs, they cannot reproduce the behavior of the user doing gestures through shoulder surfing or smudge attacks. We implemented GEAT on Samsung Focus running Windows, collected 15009 gesture samples from 50 volunteers, and conducted real-world experiments to evaluate GEAT's performance. Experimental results show that our scheme achieves an average equal error rate of 0.5 with 3 gestures using only 25 training samples. 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. Recent researches have shown that motion sensors may be used as a side channel to infer keystrokes on the touchscreen of smartphones. However, the practicality of this attack is unclear. For example, does this attack work on different devices, screen dimensions, keyboard layouts, or keyboard types? Does this attack depend on specific users or is it user independent? To answer these questions, we conducted a user study where 21 participants typed a total of 47,814 keystrokes on four different mobile devices in six settings. Our results show that this attack remains effective even though the accuracy is affected by user habits, device dimension, screen orientation, and keyboard layout. On a number-only keyboard, after the attacker tries 81 4-digit PINs, the probability that she has guessed the correct PIN is 65 , which improves the accuracy rate of random guessing by 81 times. Our study also indicates that inference based on the gyroscope is more accurate than that based on the accelerometer. We evaluated two classification techniques in our prototype and found that they are similarly effective. Current standard PIN entry systems for mobile devices are not safe to shoulder surfing. In this paper, we present VibraInput, a two-step PIN entry system based on the combination of vibration and visual information for mobile devices. This system only uses four vibration patterns, with which users enter a digit by two distinct selections. We believe that this design secures PIN entry, and allows users to easily remember and recognize the patterns. Moreover, it can be implemented on current off-the-shelf mobile devices. We designed two kinds of prototypes of VibraInput. The experiment shows that the mean failure rate is 4.0 ; moreover, the system shows good security properties.