Internet of Things (IOT) makes the smart devices eventually as stepping stones for cyber-physical smart pervasive framework development. IOT is a dynamic global network infrastructure where large amount of information is being transferred through network with interoperable communication protocols. Research and innovation is being carried out in the areas of IOT on semantic interoperability, cyber-physical system, security, and various network technologies. Security is the major challenge among these different areas in the fields of IOT. In this paper, we propose a new conceptual solution to enhance the security in IOT applications based on the feature level fusion of iris and fingerprint biometric traits through deep learning feature extractor Extreme Learning Machine (ELM). Forward Error Correction (FEC) is employed on the fused multimodal biometric traits to encrypt and secure the biometric data.

Extreme Learning Machine; Internet of Things; Information; Multimodal Biometrics; Security

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