Abstract
The Internet of Things refers to an interrelated connected network of smart devices, sensors, and embedded computers that store, process, and communicate heterogeneous data. As an emerging technology breakthrough, IoT has enabled the collection, processing, and communication of information for smart applications. These novel features have attracted city designers and health professionals as IoT are gaining popularity in real-time applications such as eHealth and smart homes. As the demand is increasing so security would be the primary concern for adopting smart home applications. To solve the security issue, we introduced a correlation and regression-based intrusion detection system for IoT smart home applications. In this paper, the clustering technique has been used to improve the results. We have also evaluated the performance on the basis of the true positive rate and the false positive rate. Our results show the 99% true positive rate in the comparison of the state of the art techniques.
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This article is part of the topical collection “Cyber Security and Privacy in Communication Networks” guest edited by Rajiv Misra, R K Shyamsunder, Alexiei Dingli, Natalie Denk, Omer Rana, Alexander Pfeiffer, Ashok Patel and Nishtha Kesswani.
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Choudhary, S., Dey, A. & Kesswani, N. CRIDS: Correlation and Regression-Based Network Intrusion Detection System for IoT. SN COMPUT. SCI. 2, 168 (2021). https://doi.org/10.1007/s42979-021-00555-2
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DOI: https://doi.org/10.1007/s42979-021-00555-2