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Heuristic vs Metaheuristic Method: Improvement of Spoofed Fingerprint Identification in IoT Devices

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The Internet of Thing (IoT) has become important due to the development of information technology that connects several aspects such as devices, services, and humans. Many IoT devices have been implemented the security system that uses biometrics sensor. The security system needs to secure the personal activity data recorded in IoT devices. Fingerprint is the one of common biometrics used in authentication but it has several security issues. Spoofed fingerprint is one of the risk security problems in biometrics authentication. This paper focuses on improving  the security aspect in biometrics that is fundamental in IoT devices. Some optimization methods such as Nelder Mead, heuristic and Simulated Annealing and metaheuristic have been used to optimize the spoofed fingerprints identification with Back Propagation Neural Network classifier. Gray Level Co-occurrence Matrix is also considered as feature extraction of fingerprints in this research. The result of this study has shown that Nelder Mead method has a better improvement accuracy with a significant of two-tail p-value equal to 0.013. Moreover, Simulated Annealing method has been able to improve the accuracy in identification of spoofed fingerprint faster than Nelder Mead, but unsignificantly.
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Backpropagation; GLCM; Heuristic; IoT; Metaheuristic; Nelder Mead; Optimization; Spoofed Fingerprint; Simulated Annealing

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