TY - GEN
T1 - SEFL
T2 - 21st International Conference on Advanced Communication Technology, ICACT 2019
AU - Mowla, Nishat I.
AU - Doh, Inshil
AU - Chae, Kijoon
N1 - Funding Information:
ACKNOWLEDGMENT The work was supported by the National Research Foundation of Korea (NRF) funded by the Korea government (MSIP) (2016R1A2B4015899). Kijoon Chae is corresponding author
Funding Information:
——————————————————————— Manuscript received on Jan. 15, 2018. This work is sponsored by Basic Science Research Program through the National Research Foundation of Korea(NRF) grant funded by the Korea government (MSIP), and a follow-up of the invited journal to the accepted & presented paper of the 19th International Conference on Advanced Communication Technology (ICACT2017), and Grant ID is 2016R1A2B4015899. Kijoon Chae is the corresponding author.
Publisher Copyright:
© 2019 Global IT Research Institute (GIRI).
PY - 2019/4/29
Y1 - 2019/4/29
N2 - User authentication in a Medical Cyber Physical Systems (MCPS) can be effectively done using biometric features. Biometric features, widely used for user authentication, are equally important to national and global technology systems. Biometric features, such as face, iris, fingerprint, are commonly used while more recently palm, vein and gait are also getting attention. To fail the traditional biometric detection systems, various spoofing approaches have also been developed over time. Among various methods, image synthesis with play-doh, gelatin, ecoflex etc. are some of the more common ways for spoofing bio-modalities. Success of traditional detection systems are related to custom tailored solutions where feature engineering for each attack type must be developed. However, this is not a feasible process when we consider countless attack possibilities. Also, a slight change in the attack can cause the whole system to be redesigned and therefore becomes a limiting constraint. The recent success of machine learning inspires this paper to explore weak and strong learners with ensemble learning approaches using AdaBoost. In essence, the paper proposes a selective ensemble fuzzy learner approach using Ada Boost, feature selection and combination of weak and strong learners to enhance the detection of bio-modality spoofing for MCPS. Our proposal was experimented on real datasets and verified on the fingerprint and iris benchmark.
AB - User authentication in a Medical Cyber Physical Systems (MCPS) can be effectively done using biometric features. Biometric features, widely used for user authentication, are equally important to national and global technology systems. Biometric features, such as face, iris, fingerprint, are commonly used while more recently palm, vein and gait are also getting attention. To fail the traditional biometric detection systems, various spoofing approaches have also been developed over time. Among various methods, image synthesis with play-doh, gelatin, ecoflex etc. are some of the more common ways for spoofing bio-modalities. Success of traditional detection systems are related to custom tailored solutions where feature engineering for each attack type must be developed. However, this is not a feasible process when we consider countless attack possibilities. Also, a slight change in the attack can cause the whole system to be redesigned and therefore becomes a limiting constraint. The recent success of machine learning inspires this paper to explore weak and strong learners with ensemble learning approaches using AdaBoost. In essence, the paper proposes a selective ensemble fuzzy learner approach using Ada Boost, feature selection and combination of weak and strong learners to enhance the detection of bio-modality spoofing for MCPS. Our proposal was experimented on real datasets and verified on the fingerprint and iris benchmark.
KW - Biometric spoofing
KW - Ensemble Learning
KW - Feature selection
KW - MCPS
KW - Spoofing Detection
UR - http://www.scopus.com/inward/record.url?scp=85065650193&partnerID=8YFLogxK
U2 - 10.23919/ICACT.2019.8701916
DO - 10.23919/ICACT.2019.8701916
M3 - Conference contribution
AN - SCOPUS:85065650193
T3 - International Conference on Advanced Communication Technology, ICACT
SP - 1153
EP - 1158
BT - 21st International Conference on Advanced Communication Technology
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 17 February 2019 through 20 February 2019
ER -