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Today, Internet of Things (IoT) services has been increasing extensively because of their optimum device sizes and their developed network infrastructure that includes devices based on internet embedded with various sensors, actuators, communication, and storage components providing connection and data exchange. Presently number of industries use vast number of IoT devices, there are some challenges like reducing the risks and threats that exposure, accommodating the huge number of IoT devices in network and providing secure vulnerabilities have risen. Supervised learning has recently been gaining popularity to provide device classification. But this supervised learning became unrealistic as producing millions of new IoT devices each year, and insufficient training data. In this paper, security framework connection assistance for IoT device secured data communication is proposed. A multi-level security support architecture which combines clustering technique with deep neural networks for designing the resource oriented IoT devices with high security and these are enabling both the seen and unseen device classification. The datasets dimensions are reduced by considering the technique as auto encoder. Therefore in between accuracy and overhead classification good balancing is established. The comparative results are describes that proposed security system is better than remaining existing systems.

Security Framework Connection Assistance for IoT Device Secure Data communication