| Title | Binarized Multi-Factor Cognitive Detection of Bio-Modality Spoofing in Fog Based Medical Cyber-Physical System |
| Publication Type | Conference Paper |
| Year of Publication | 2019 |
| Authors | Mowla, Nishat I, Doh, Inshil, Chae, Kijoon |
| Conference Name | 2019 International Conference on Information Networking (ICOIN) |
| Date Published | jan |
| Keywords | authentication, binarized multifactor cognitive detection, bio-modality detection sensors, bio-modality detection systems, bio-modality spoofing, Biomedical imaging, biometrics (access control), Computer architecture, Cyber-physical systems, Ensemble Learning, face recognition, feature extraction, feature selection, fingerprint identification, fog based architectures, Fog Computing, fog node, fog-based security application, heavy-weight detection mechanisms, human factors, image synthesis, Iris recognition, learning (artificial intelligence), MCPS, medical cyber-physical systems, Metrics, multifactor authentication, pubcrawl, Resiliency, resource-constrained sensors, secure user authentication, security of data, Sensor management, Sensors, spoofing bio-identifiable property, spoofing detection, telecommunication security, Wireless sensor networks |
| Abstract | 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. |
| DOI | 10.1109/ICOIN.2019.8718118 |
| Citation Key | mowla_binarized_2019 |
Binarized Multi-Factor Cognitive Detection of Bio-Modality Spoofing in Fog Based Medical Cyber-Physical System