TY - GEN
T1 - Binarized Multi-Factor Cognitive Detection of Bio-Modality Spoofing in Fog Based Medical Cyber-Physical System
AU - Mowla, Nishat I.
AU - Doh, Inshil
AU - Chae, Kijoon
N1 - Funding Information:
The work was supported by the National Research Foundation of Korea (NRF) funded by the Korea government (MSIP) (2016R1A2B4015899). Kijoon Chae is the corresponding author.
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 the corresponding author.
Publisher Copyright:
© 2019 IEEE.
PY - 2019/5/17
Y1 - 2019/5/17
N2 - Bio-modalities are ideal for user authentication in Medical Cyber-Physical Systems. Various forms of bio-modalities, such as the face, iris, fingerprint, are commonly used for secure user authentication. Concurrently, various spoofing approaches have also been developed over time which can fail traditional bio-modality detection systems. Image synthesis with play-doh, gelatin, ecoflex etc. are some of the ways used in spoofing bio-identifiable property. Since the bio-modality detection sensors are small and resource constrained, heavy-weight detection mechanisms are not suitable for these sensors. Recently, Fog based architectures are proposed to support sensor management in the Medical Cyber-Physical Systems (MCPS). A thin software client running in these resource-constrained sensors can enable communication with fog nodes for better management and analysis. Therefore, we propose a fog-based security application to detect bio-modality spoofing in a Fog based MCPS. In this regard, we propose a machine learning based security algorithm run as an application at the fog node using a binarized multi-factor boosted ensemble learner algorithm coupled with feature selection. Our proposal is verified on real datasets provided by the Replay Attack, Warsaw and LiveDet 2015 Crossmatch benchmark for face, iris and fingerprint modality spoofing detection used for authentication in an MCPS. The experimental analysis shows that our approach achieves significant performance gain over the state-of-The-Art approaches.
AB - Bio-modalities are ideal for user authentication in Medical Cyber-Physical Systems. Various forms of bio-modalities, such as the face, iris, fingerprint, are commonly used for secure user authentication. Concurrently, various spoofing approaches have also been developed over time which can fail traditional bio-modality detection systems. Image synthesis with play-doh, gelatin, ecoflex etc. are some of the ways used in spoofing bio-identifiable property. Since the bio-modality detection sensors are small and resource constrained, heavy-weight detection mechanisms are not suitable for these sensors. Recently, Fog based architectures are proposed to support sensor management in the Medical Cyber-Physical Systems (MCPS). A thin software client running in these resource-constrained sensors can enable communication with fog nodes for better management and analysis. Therefore, we propose a fog-based security application to detect bio-modality spoofing in a Fog based MCPS. In this regard, we propose a machine learning based security algorithm run as an application at the fog node using a binarized multi-factor boosted ensemble learner algorithm coupled with feature selection. Our proposal is verified on real datasets provided by the Replay Attack, Warsaw and LiveDet 2015 Crossmatch benchmark for face, iris and fingerprint modality spoofing detection used for authentication in an MCPS. The experimental analysis shows that our approach achieves significant performance gain over the state-of-The-Art approaches.
KW - Bio-modality spoofing
KW - Ensemble Learning
KW - Feature selection
KW - Fog computing
KW - MCPS
KW - Spoofing Detection
UR - http://www.scopus.com/inward/record.url?scp=85066758910&partnerID=8YFLogxK
U2 - 10.1109/ICOIN.2019.8718118
DO - 10.1109/ICOIN.2019.8718118
M3 - Conference contribution
AN - SCOPUS:85066758910
T3 - International Conference on Information Networking
SP - 43
EP - 48
BT - 33rd International Conference on Information Networking, ICOIN 2019
PB - IEEE Computer Society
Y2 - 9 January 2019 through 11 January 2019
ER -