Wrist-wearables such as smartwatches and fitness bands are equipped with a variety of high-precision sensors that support novel contextual and activity-based applications. The presence of a diverse set of on-board sensors, however, also expose an additional attack surface which, if not adequately protected, could be potentially exploited to leak private user information. In this paper, we investigate the feasibility of a new attack that takes advantage of a wrist-wearable's motion sensors to infer input on mechanical devices typically used to secure physical access, for example, combination locks. We outline an inference framework that attempts to infer a lock's unlock combination from the wrist motion captured by a smartwatch's gyroscope sensor, and uses a probabilistic model to produce a ranked list of likely unlock combinations. We conduct a thorough empirical evaluation of the proposed framework by employing unlocking-related motion data collected from human subject participants in a variety of controlled and realistic settings. Evaluation results from these experiments demonstrate that motion data from wrist-wearables can be effectively employed as a side-channel to significantly reduce the unlock combination search-space of commonly found combination locks, thus compromising the physical security provided by these locks.
In this work, we investigate the feasibility of a new kind of privacy threat, i.e., inferring unlock combinations of mechanical locks using wrist-wearable motion sensors, which has never been investigated before. Several modern smart locks offer a numeric keypad which can be compromised using known smartwatch-based keystroke inference techniques in the literature @cite_23 @cite_53 @cite_49 @cite_12 @cite_25 . However, in this work we target traditional rotation-based mechanical locks which are still very popular and where existing attack techniques will not work. Blaze @cite_42 @cite_28 systematically examined physical and design weaknesses in both combination and pin-tumbler locks. However, our primary contribution in this work is to show how external side-channel attacks can make even a securely designed lock vulnerable.
@cite_28 @cite_53 @cite_42 @cite_49 @cite_23 @cite_25 @cite_12
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PIN unlock is a popular screen locking mechanism used for protecting the sensitive private information on smart-phones. However, it is susceptible to a number of attacks such as guessing attacks, shoulder surfing attacks, smudge attacks, and side-channel attacks. Scramble keypad changes the keypad layout in each PIN-entry process to improve the security of PIN unlock. In this paper, the security and usability of scramble keypad for PIN unlock are studied. Our security analysis shows that scramble keypad can defend smudge attacks perfectly and greatly reduce the threats of side-channel attacks. A user study is conducted to demonstrate that scramble keypad has a better chance to defend shoulder surfing attacks than standard keypad. We also investigate how the usability of scramble keypad is compromised for improved security through a user study. In this paper, we investigate the feasibility of keystroke inference attacks on handheld numeric touchpads by using smartwatch motion sensors as a side-channel. The proposed attack approach employs supervised learning techniques to accurately map the uniqueness in the captured wrist movements to each individual keystroke. Experimental evaluation shows that keystroke inference using smartwatch motion sensors is not only fairly accurate, but also better than similar attacks previously demonstrated using smartphone motion sensors. Graphical passwords that allow a user to unlock a smartphone's screen are one of the Android operating system's features and many users prefer them instead of traditional text-based codes. A variety of attacks has been proposed against this mechanism, of which notable are methods that recover the lock patterns using the oily residues left on screens when people move their fingers to reproduce the unlock code. In this paper we present a pilot study on user habits when setting a pattern lock and on their perceptions regarding what constitutes a secure pattern. We use our survey's results to establish a scheme, which combines a behaviour-based attack and a physical attack on graphical lock screen methods, aiming to reduce the search space of possible combinations forming a pattern, to make it partially or fully retrievable. One rising trend in today's consumer electronics is the wearable devices, e.g., smartwatches. With tens of millions of smartwatches shipped, however, the security implications of such devices are not fully understood. Although previous studies have pointed out some privacy concerns about the data that can be collected, like personalized health information, the threat is considered low as the leaked data is not highly sensitive and there is no real attack implemented. In this paper we investigate a security problem coming from sensors in smartwatches, especially the accelerometer. The results show that the actual threat is much beyond people's awareness. Being worn on the wrist, the accelerometer built within a smartwatch can track user's hand movements, which makes inferring user inputs on keyboards possible in theory. But several challenges need to be addressed ahead in the real-world settings: e.g., small and irregular hand movements occur persistently during typing, which degrades the tracking accuracy and sometimes even overwhelms useful signals. In this paper, we present a new and practical side-channel attack to infer user inputs on keyboards by exploiting sensors in smartwatch. Novel keystroke inference models are developed to mitigate the negative impacts of tracking noises. We focus on two major categories of keyboards: one is numeric keypad that is generally used to input digits, and the other is QWERTY keyboard on which a user can type English text. Two prototypes have been built to infer users' banking PINs and English text when they type on POS terminal and QWERTY keyboard respectively. Our results show that for numeric keyboard, the probability of finding banking PINs in the top 3 candidates can reach 65 , while for QWERTY keyboard, a significant accuracy improvement is achieved compared to the previous works, especially of the success rate of finding the correct word in the top 10 candidates.