TY - GEN
T1 - Geometrical analysis of machine learning security in biometric authentication systems
AU - Sadeghi, Koosha
AU - Banerjee, Ayan
AU - Sohankar, Javad
AU - Gupta, Sandeep
N1 - Funding Information:
This work has been partly funded by CNS grant #1218505, IIS grant #1116385, and NIH grant #EB019202.
Funding Information:
∗This work has been partly funded by CNS grant #1218505, IIS grant #1116385, and NIH grant #EB019202.
Publisher Copyright:
© 2017 IEEE.
PY - 2017
Y1 - 2017
N2 - Feature extraction and Machine Learning (ML) techniques are required to reduce high variability of biometric data in Biometric Authentication Systems (BAS) toward improving system utilization (acceptance of legitimate subjects). However, reduction in data variability, also decreases the adversary's effort in manufacturing legitimate biometric data to break the system (security strength). Typically for BAS design, security strength is evaluated through variability analysis on data, regardless of feature extraction and ML, which are essential for accurate evaluation. In this research, we provide a geometrical method to measure the security strength in BAS, which analyzes the effects of feature extraction and ML on the biometric data. Using the proposed method, we evaluate the security strength of five state-of-the-art electroencephalogram-based authentication systems, on data from 106 subjects, and the maximum achievable security strength is 83 bits.
AB - Feature extraction and Machine Learning (ML) techniques are required to reduce high variability of biometric data in Biometric Authentication Systems (BAS) toward improving system utilization (acceptance of legitimate subjects). However, reduction in data variability, also decreases the adversary's effort in manufacturing legitimate biometric data to break the system (security strength). Typically for BAS design, security strength is evaluated through variability analysis on data, regardless of feature extraction and ML, which are essential for accurate evaluation. In this research, we provide a geometrical method to measure the security strength in BAS, which analyzes the effects of feature extraction and ML on the biometric data. Using the proposed method, we evaluate the security strength of five state-of-the-art electroencephalogram-based authentication systems, on data from 106 subjects, and the maximum achievable security strength is 83 bits.
KW - geometrical analysis
KW - machine learning
KW - security
UR - http://www.scopus.com/inward/record.url?scp=85048481973&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85048481973&partnerID=8YFLogxK
U2 - 10.1109/ICMLA.2017.0-142
DO - 10.1109/ICMLA.2017.0-142
M3 - Conference contribution
AN - SCOPUS:85048481973
T3 - Proceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017
SP - 309
EP - 314
BT - Proceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017
A2 - Chen, Xuewen
A2 - Luo, Bo
A2 - Luo, Feng
A2 - Palade, Vasile
A2 - Wani, M. Arif
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017
Y2 - 18 December 2017 through 21 December 2017
ER -