Researchers from Binghamton University and the Stevens Insititute of Technology have developed an algorithm, the Backward PIN-sequence Inference Algorithm, that can collect motion data from wearables and guess PINs and passwords with an 80% accuracy rate on the first try. The algorithm is based off a technique called Forward Pinning which uses a series of mathematical operations to determine whether or not two objects are in close proximity by comparing their distance between each other.
Researchers from Binghamton University and the Stevens Insititute of Technology have developed an algorithm, the Backward PIN-sequence Inference Algorithm, that can collect motion data from wearables and guess PINs and passwords with an 80% accuracy rate on the first try. One way to counter an attack, the researchers suggest, is for developers to insert noise data that would disrupt motion-data collection.