The growing demand for an independent and comfortable lifestyle has motivated the development of the smart home, and providing security is a major challenge for developers and security analysts. Enhancing security in the home environment has been recognized as one of the main obstacles to realizing the vision of creating energy-efficient smart homes and buildings. Understanding the risks associated with the use and potential exploitation of information about homes, end-users, and partners, as well as forming techniques for integrating security assessments into the design, is not straightforward. To address this challenge, we propose enhanced secure network architecture (SH-SecNet) for the diagnosis of security threats in the smart home. In our architecture, we use the Multivariate Correlation Analysis (MCA) technique to analyze the network flow packet in the network layer, as this classifies the network traffic by extracting the correlation between network traffic features. We evaluated the performance of our architecture with respect to various parameters, such as CPU utilization, throughput, round trip time, and accuracy. The result of the evaluation shows that our architecture is efficient and accurate in detecting and mitigating attacks in the smart home network with a low performance overhead.
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