Limitations of Scanned Human Copresence Encounters for Modelling Proximity-Borne Malware
Patterns of human encounters, which are difficult to observe directly, are fundamental to the propagation of mobile malware aimed at infecting devices in spatial proximity. The authors investigate errors introduced by using scanners that detect the presence of devices on the assumption that device co-presence at a scanner corresponds to a device encounter. They show in an ideal static model that only 59% of inferred encounters correspond to actual device co-presence. To investigate the effects of mobility, they use a simulator to compare encounters between devices with those inferred by scanners.