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Keywords

IoT Machine Learning Network Traffic Security RT-IoT2022

Doi : 10.5281/zenodo.18664507

Abstract

This study aims to analyze the monitoring of network traffic and the detection of security incidents arising from the widespread use of Internet of Things (IoT) devices, which, despite making daily life more convenient, also pose significant security risks. Methodologies such as DDoS, data exfiltration, spoofing, scanning, and brute-force attacks occurring within a network threaten both individual users and corporate systems. In this study, the RT-IoT2022 dataset derived from a real-time IoT infrastructure and containing both normal and malicious network behaviors consistent with real-world scenarios has been utilized. By using this dataset, the objective is to examine attack methodologies on IoT network traffic through machine learning techniques and to propose an effective detection model capable of identifying various types of attacks.

References

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