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IoT Attack Detection: Learning Approaches by Nevetha Govindaraj is a document available to read on EtoBox.

This thesis investigates the effectiveness of supervised and unsupervised machine learning algorithms for detecting attacks on Internet of Things (IoT) devices using two datasets: CICIoT2022 and Bot-IoT. It evaluates various machine learning techniques, including Decision Trees, Support Vector Machines, and K-Nearest Neighbors for supervised learning, and Local Outlier Factor and Isolation Forest for unsupervised learning, to determine their performance in identifying malicious traffic. The findings aim to

Author

Nevetha Govindaraj

Language

EN