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Analysis of the Impact of Dimensionality Reduction on the Accuracy and Performance of Intrusion Detection Systems in IoT Environments

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dc.contributor.author Zedouri, Amin
dc.date.accessioned 2023-11-28T12:26:28Z
dc.date.available 2023-11-28T12:26:28Z
dc.date.issued 2023
dc.identifier.uri http://dspace.univ-guelma.dz/jspui/handle/123456789/15050
dc.description.abstract The Internet of Things (IoT) has transformed technology by facilitating seamless com- munication and data exchange among interconnected devices. However, this in- creased connectivity poses security challenges, necessitating intrusion detection sys- tems (IDS) to protect IoT environments. This study examines the influence of dimen- sionality reduction methods on IDS accuracy and performance in IoT. We analyze various dimensionality reduction techniques and their impact on IoT intrusion detec- tion systems. Four machine learning models (linear regression, decision tree, SVM, MLP) are implemented with principal component analysis (PCA) as the chosen re- duction method. The IoTID20 dataset is used for training and testing. Comparative evaluations with existing algorithms measure accuracy, F1-score, fit time, and score time. Results reveal that PCA significantly reduces training time without significant accuracy loss. This research offers insights into the impact of dimensionality reduc- tion on IDS performance in IoT, highlighting PCA’s advantages in optimizing training time. en_US
dc.language.iso en en_US
dc.publisher University of Guelma en_US
dc.subject Internet of Things, security, intrusion detection systems (IDS), machine learning, dimensionality reduction methods, IoT environments, IoTID20. en_US
dc.title Analysis of the Impact of Dimensionality Reduction on the Accuracy and Performance of Intrusion Detection Systems in IoT Environments en_US
dc.type Working Paper en_US


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