Abstract:
Due to the significant privacy risks that smartphones present and due to
their importance in device authentication and forensics investigations,
fingerprinting smartphones have become increasingly popular. This thesis
is focused on accelerometers, loudspeakers, microphones and camera
sensors as potential fingerprint sources for smartphone embedded transducers.
While there is little user knowledge regarding the privacy dangers,
the output of these transducers, which convert one form of energy
into another, leaks across numerous channels, like social networks, mobile
apps and cloud services. Several signal processing techniques are
used to extract characteristics and various traditional machine-learning
algorithms are employed to fingerprint different and identical sensors.
This thesis also proposes a system for device pairing based on accelerometer
data collected from several transportation modes, i.e., tram,
train, car, bike, walk and shake. In addition to smartphone sensors fingerprinting,
ECU (Electronic Control Unit) fingerprinting is discussed
as an extension.
