Intrusion detection in the IoT data streams using concept drift localization

Intrusion detection in the IoT data streams using concept drift localization

With the widespread application of smart devices, the security of internet of things (IoT) systems faces entirely new challenges. The IoT data stream operates in a non-stationary, dynamic environment, making it prone to concept drift. This paper focused on addressing the issue of concept drift in da...

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Bibliographic Details
Main Authors: Renjie Chu, Peiyuan Jin, Hanli Qiao, Quanxi Feng
Format: Article
Language:English
Published: AIMS Press 2024-01-01
Series:AIMS Mathematics
Subjects:
iot
network attack detection
concept drift
non-parametric
xgboost
Online Access:https://www.aimspress.com/article/doi/10.3934/math.2024076?viewType=HTML

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https://www.aimspress.com/article/doi/10.3934/math.2024076?viewType=HTML

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