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 percent 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.
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.