"A Hand Structure-Based Mobile Authentication Solution to the Security-Reliability Trade-off"

Hand structure-based mobile authentication is a viable alternative to current mobile authentication techniques due to its incorporation of security, reliability, privacy and convenience. With privacy and convenience inherent to this mode of authentication, the research leverages hand biometrics and the K-Nearest Neighbors, Support Vector Machines, and Bagged Decision Trees machine learning algorithms to examine the security-reliability trade-off. The results show that there are statistically significant properties present in structure-borne signals that enable differentiation between individuals and hand positions. The repository includes code for data collection, preprocessing, and analysis. It also includes zip files for the models developed during research.

Citation:

S. Chatiwala et al., "A Secure and Reliable Mobile Authentication Alternative Utilizing Hand Structure," 2022 IEEE MIT Undergraduate Research Technology Conference (URTC), 2022, pp. 1-5, doi: 10.1109/URTC56832.2022.10002184.

Repository Structure:

Folders:

• classes: contains data structures to organize data
• functions: contains functions to calculate signal information
• graphs: contains MATLAB figures for transmissions, signals, segments, and chirps of each subject
• recordings: contains audio files from user authentication experiments, subfolders for individual users
• transmissions: contains chirp audio files transmitted by mobile device

Classes:

• profile.m: structure for user data, holds user-influenced chirp signal and features extracted

Functions:

• create_graphs_from_mats.m: creates graphs of segmented chirps based on array index range specified as parameters
• func_chirp_gen.m: generates chirp signal based on parameters provided to it
• get_chirp_interval.m: returns array indices of the nth chirp specified as a parameter; used with create_graphs_mats.m
• get_features: calculates time domain features when given user-influenced chirp signal
• get_freq_data: calculates frequency domain features such as mfcc, called by get_features
• mfcc.m: calculates mfcc variables
• trifbank.m: used by mfcc.m
• vec2frames.m: used by mfcc.m

Steps for Data Collection and Preprocessing:

• Step 1: create_chirp.m: choose what type of chirp signal to create, saves .wav file
• Step 2: cross_correlate.m: select audio file from audio folder and extract user-influenced signal from recording, creates profile
• Step 3: feature_extraction.m: given user profile, calculate the features, saves user data into profile
• Step 4: learning.m: compiles all desired profiles into one data structure for Classification Learner's Application

Software Requirements:

MATLAB Toolboxes:

In order to run the code in the repository, it is necessary to install the following MATLAB Toolboxes:

• Signal Processing Toolbox
• Antenna Toolbox
• Communications Toolbox
• Fixed-Point Designer
• Statistics and Machine Learning Toolbox

Link to Existing Classification Learner Models:

• Download and unzip the following file: https://drive.google.com/file/d/1QkFw_0h__8nd4x2uBvWdolyk5szkW49y/view?usp=sharing

Classification Learners Application

• In MATLAB, open the APPS tab at the top of the screen
• Click the Classification Learner icon (looks like bunch of red and blue dots)
• Start a new learning session by loading your desired workspace data (master_data created by learning.m)
• Models were developed by experimenting with different architectures and modifying false validation costs to help the models specialize for different authentication scenarios

Android Mobile Device

• Voice recording software
• "chirp_18khzto22khz_48khzfs_25ms_repeat40.wav" file from transmissions folder saved as a .wav file

Authors:

Sahil Chatiwala, Reva Hajarnis, Alexei Korolev, Danielle Park, David Shenkerman, Dr. Yingying Chen, and Yilin Yang (PhD Student)