Visible to the public Towards Inferring Mechanical Lock Combinations Using Wrist-Wearables As a Side-Channel

TitleTowards Inferring Mechanical Lock Combinations Using Wrist-Wearables As a Side-Channel
Publication TypeConference Paper
Year of Publication2018
AuthorsMaiti, Anindya, Heard, Ryan, Sabra, Mohd, Jadliwala, Murtuza
Conference NameProceedings of the 11th ACM Conference on Security & Privacy in Wireless and Mobile Networks
Conference LocationNew York, NY, USA
ISBN Number978-1-4503-5731-9
KeywordsHuman Behavior, pubcrawl, Resiliency, Scalability, wearables security
AbstractWrist-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.
Citation Keymaiti_towards_2018