Shoulder-Surfing Safe Login in a Partially Observable Attacker Model

Abstract

AbstractSecure login methods based on human cognitive skills can be classified into two categories based on information available to a passive attacker: (i) the attacker fully observes the entire input and output of a login procedure, (ii) the attacker only partially observes the input and output. Login methods secure in the fully observable model imply very long secrets and/or complex calculations. In this paper, we study three simple PIN-entry methods designed for the partially observable attacker model. A notable feature of the first method is that the user needs to perform a very simple mathematical operation, whereas, in the other two methods, the user performs a simple table lookup. Our usability study shows that all the methods have reasonably low login times and minimal error rates. These results, coupled with low-cost hardware requirements (only earphones), are a significant improvement over existing approaches for this model [9,10]. We also show that side-channel timing attacks present a real threat to the security of login schemes based on human cognitive skills.KeywordsUsability StudyMod10 MethodHigh Error RateSystem Usability ScaleMinimal Error RateThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.