Smartphone Fingerprinting Combining Features of On-Board Sensors

Abstract

Many everyday activities involve the exchange of confidential information through the use of a smartphone in mobility i.e. sending on e-mail, checking bank account, buying on-line, accessing cloud platforms, health monitoring. This demonstrates how security issues related to these operations are a major challenge in our society and in particular in the cybersecurity domain. The proposed paper focuses on the use of the smartphone intrinsic and physical characteristics as a mean to build a smartphone fingerprint to enable devices identification. The basic idea proposed in this paper is to investigate how to generate a specific fingerprint that allows to distinctively and reliably characterize each smartphone. In particular, the accelerometer, the gyroscope, the magnetometer and the audio system (microphone-speaker) are taken into account to build up a composite fingerprint based on a set of their distinctive features. Many experiments have been carried out, by analyzing different classification methods, diverse features combination configurations and operative scenarios. Satisfactory results have been obtained showing that the combination of such sensors improves smartphone distinctiveness.

Sensors of Smart Devices in the Internet of Everything (IoE) Era: Big Opportunities and Massive Doubts

“…Many smartphone applications have been written to utilize these maps to debug written applications and study user behaviors when debugging application issues, such as Appsee [15]. Moreover, many works have been conducted to study touch gesture utilizing touch maps for health diagnoses, such as Down syndrome [16], perceived difficulty [17], and issues in fine motor skills and eyes [18].…”

“…Mean [18,74,75,92,95,[180][181][182][183][184] The summation of data points divided by their number Std deviation [18,58,74,75,180,183,184] It is the square root of variance Average deviation [18,58,74,180,183] The average separation of data points from their mean or average value Skewness [18,180,183] Measures the asymmetry from the mean value. It utilizes the mean and the variance Kurtosis [18,180,183] Estimates the frequency of extreme values. It utilizes the mean value in its formula RMS amplitude [18,180,183,184] It is leveraged to calculate the power of a signal.…”

“…It utilizes the mean and the variance Kurtosis [18,180,183] Estimates the frequency of extreme values. It utilizes the mean value in its formula RMS amplitude [18,180,183,184] It is leveraged to calculate the power of a signal. It utilizes the maximum value of a set Lowest value [18,180,[182][183][184] The maximum data point Highest value [18,180,[182][183][184] The minimum data point ZCR [18,183] Zero crossing rate is a counter of how many times the data points cross the zero value Nonnegative count [18,183] Total number of positive data points in a set Average absolute difference [75,185] The average of the total differences between all data points in a set Time between peaks [75,184,185] The number of points between two high peaks or low peaks Binned distribution [75] The processes of grouping data points into smaller number of points or "bins" Feature Citation mining process is not also an issue; machine learning algorithms are useful in finding models for the required focused information [44].…”

The Archeology Field in the Mobile Era: A Roadmap to Catch-up

“…However, it is worth mentioning that Android is the only open source software mobile technology platform. As a matter of fact, it is the most used mobile technology platform [30][31][32][33][34][35].…”

Trusted Cameras on Mobile Devices Based on SRAM Physically Unclonable Functions

“…Many techniques have been proposed for camera identification in the last two decades [ 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 ]. An identifier is extracted and associated with a camera as a fingerprint that makes it distinguishable from another one (of the same brand or even of the same model).…”