We demonstrate the applicability of the proposed taxonomy by introducing an expert-based procedure for deriving mobile security awareness models for different attack classes (each class is an aggregation of social engineering attacks that exploit a similar set of human vulnerabilities).
Application of the proposed procedure, based on the input of 17 security experts, to derive mobile security awareness models of four different attack classes, confirms that the skills required from a smartphone user to mitigate an attack are different for different attack classes.
The same is true for data on human mobility derived from mobile phone traces that provide detailed time-resolved information about the location of users [35].
Information on human mobility behavior derived from mobile phones has been shown to be an invaluable source to leverage within the public health domain, both at an aggregated and individual level [17].
Mobility estimates derived from mobile phone data were better able to predict the percentage of households in a sublocation missing immunizations or antenatal care than were estimates of travel times to health facilities.
These estimates depend on the sample of mobile elements included in the analysis, and therefore are very likely to under-represent the extent to which sequences derived from mobile elements are present in this forest, since the diversity of mobile elements is currently undersampled.
Authors' response: The referee's first interpretation is correct: the 10.3% depends on the sample of mobile elements included in the analysis, and therefore are very likely to underrepresent the extent to which sequences derived from mobile elements are present in this set of trees, since the diversity of mobile elements is currently undersampled.
This too was driven by the growth in the relative proportion of traffic derived from mobile.
Many of the VANET clustering algorithms are derived from mobile ad hoc networks (MANET).
Other datasets used in this study are either derived from mobile records, or publicly available.
Our research implies that features derived from mobile phone data may be useful in measuring and predicting economic development, thereby complementing scarce economic statistics in developing countries.
