A highly secure Multi- Factor authentication system using biometrics to enhance privacy in Internet of Things (IOT)
Authentication is becoming critical in the Internet of Things (IoT) environment because of its many applications and services have been emerging in the areas such as smart city, healthcare, industry etc. Security and privacy plays a vital role in IoT because their services can be accessed through smart device applications by the user from everywhere and at any time. Hence a multi-factor based authentication can provide high security in IOT environment. This security system incorporates most of the valuable methods such as cryptography, steganography and pattern recognition for authentication process. Among various biometric traits, palm vein is more efficient because it has essential sufficient features points for individual unique identification. The system employs registration phase and authentication phase. The registration phase enrolls person privacy data with their biometric and the obtained data’s are encrypted with the help of Elliptical Curve Cryptography (ECC) and this confidential information is embedded into person palm print image using bits substitution procedure. In authentication phase, recognition will be performed through three levels such as password, palm print and One Time Password (OTP). Using these three levels the matching can be done. The texture features can be obtained by using Multi Block Local Binary Pattern (MB-LBP) and Gabor filter. To afford high authentication, OTP method is also appended. This system provides better information security and texture analysis rather than previous approaches. Thus this multiple level approach ensures a fool proof and a reliable way for data access. Results are in terms of some validation parameters like false acceptance ratio, false rejection ratio and recognition rate. Observing from results, it is clear that the proposed approach outperform many existing methods. As a result, the proposed scheme has strong security, reliability and enhanced computational efficiency.
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