  Contextual proximity detection (or, co-presence detection) is a promising
approach to defend against relay attacks in many mobile authentication systems.
We present a systematic assessment of co-presence detection in the presence of
a context-manipulating attacker. First, we show that it is feasible to
manipulate, consistently control and stabilize the readings of different
acoustic and physical environment sensors (and even multiple sensors
simultaneously) using low-cost, off-the-shelf equipment. Second, based on these
capabilities, we show that an attacker who can manipulate the context gains a
significant advantage in defeating context-based co-presence detection. For
systems that use multiple sensors, we investigate two sensor fusion approaches
based on machine learning techniques: features-fusion and decisions-fusion, and
show that both are vulnerable to contextual attacks but the latter approach can
be more resistant in some cases.
