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In this paper a novel multi-factor authentication protocol for IoT applications, relying on enhanced Rabinassisted elliptic curve cryptography, biometric features and time stamping methods, is developed. Furthermore, a fuzzy verification algorithm has been developed to perform receiverlevel user verification, making computation efficient in terms of computational overhead as well as latency. An NS2 simulation-based performance assessment has revealed that the multifactor authentication and key management models we have proposed are capable of not only avoiding security breaches, such as smart card loss (SCLA) and impersonation attacks, but can also ensure the provision of maximum possible QoS levels by offering higher packet delivery and minimum latency rates.

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The rapid growth of internet and internet services provision offers wide scope to the industries to couple the various network models to design a flexible and simplified communication infrastructure. A significant attention paid towards Internet of things (IoT), from both academics and industries. Connecting and organizing of communication over wireless IoT network models are vulnerable to various security threats, due to the lack of inappropriate security deployment models. In addition to this, these models have not only security issues; they also have many performance issues. This research work deals with an IoT security over WSN model to overcome the security and performance issues by designing a Energy efficient secured cluster based distributed fault diagnosis protocol (EESCFD) Model which combines the self-fault diagnosis routing model using cluster based approach and block cipher to organize a secured data communication and to identify security fault and communication faults to improve communication efficiency. In addition we achieve an energy efficiency by employing concise block cipher which identifies the ideal size of block, size of key, number of rounds to perform the key operations in the cipher.