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Title BlockSecIoTNet: Blockchain-based decentralized security architecture for IoT network
ID_Doc 43707
Authors Rathore, S; Kwon, BW; Park, JH
Title BlockSecIoTNet: Blockchain-based decentralized security architecture for IoT network
Year 2019
Published
Abstract The exponential growth of the use of insecure stationary and portable devices in the Internet of Things (IoT) network of the smart city has made the security of the smart city against cyber-attacks a vital issue. Various mechanisms for detecting security attacks that rely on centralized and distributed architectures have already been proposed, but they tend to be inefficient due to such problems as storage constraints, the high cost of computation, high latency, and a single point of failure. Moreover, existing security mechanisms are faced with the issue of monitoring and collecting historic data throughout the entire IoT network of the smart city in order to deliver optimal security and defense against cyberattacks. To address the current challenges, this paper proposes a decentralized security architecture based on Software Defined Networking (SDN) coupled with a blockchain technology for IoT network in the smart city that relies on the three core technologies of SDN, Blockchain, and Fog and mobile edge computing in order to detect attacks in the IoT network more effectively. Thus, in the proposed architecture, SDN is liable to continuous monitoring and analysis of traffic data in the entire IoT network in order to provide an optimal attack detection model; Blockchain delivers decentralized attack detection to mitigate the "single point of failure" problem inherent to the existing architecture; and Fog and mobile edge computing supports attack detection at the fog node and, subsequently, attack mitigation at the edge node, thus enabling early detection and mitigation with lesser storage constraints, cheaper computation, and low latency. To validate the performance of the proposed architecture, it was subjected to an experimental evaluation, the results of which show that it outperforms both centralized and distributed architectures in terms of accuracy and detection time.
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