Abstract
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.
Highlights
The Internet of Things (IoT) technology is widely spread around us because of its high level of security and provides best privacy to the system [1]
The proposed architecture is shown in fig. 1 which is used for enabling the security operation for IoT devices without increasing the processing load
In experiment results, proposed security framework for Iot performance evaluation is divided into two parts: OPTICS unsupervised device type identification and Random forest supervised dimension reduction for anomaly detection
Summary
The Internet of Things (IoT) technology is widely spread around us because of its high level of security and provides best privacy to the system [1]. If there is increment in connected devices in a network through internet estimation is created by IoT as billions of users are crossed till 2020 [2]. Number of devices is connected with internet through the Internet of Things (IoT). Data confidentiality, privacy, authorization and authentication are IoT main security issues [16]. The attacker was entered into the communication at hardware layer of IoT device and security parameters. The device performance is detected first in the proposed architecture and securing operation is being processed to the IoT devices. The combination of clustering technique with supervised learning is proposed in this paper for enabling device seen and unseen type classification, the difference between secured IoT networks and unauthorized device accessing networks are detected[9]. Datasets dimensionality is reduced with proposed auto encoder technique which resulting the good accuracy and load balancing
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