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- W4313307614 abstract "Denial of Service ( <tex xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>$D$</tex> oS) attacks are a major threat for vehicular networks. Detecting and identifying the <tex xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>$D$</tex> oS traffic is crucial for defending against such attacks. Machine Learning (ML) algorithms have been extensively adopted in traffic classification and detection of network attacks, namely the DoS attacks. Among and unlike different ML learning models, Unsupervised Learning (UL) algorithms have not being used in the literature for DoS detection. This paper shed the light on the feasibility of using unsupervised learning algorithms for detecting DoS attacks. It analyzes and compares the detection efficiency of selected UL algorithms using the Vehicular Reference Misbehavior (VeReMi) dataset [1]. Finally, simulation demonstrates the performance and efficiency of the used UL algorithms in <tex xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>$D$</tex> oS detection; in particular, the Gaussian Mixture Model (GMM) algorithm demonstrates a detection accuracy with more than 95% for all <tex xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>$D$</tex> oS attack traffic categories." @default.
- W4313307614 created "2023-01-06" @default.
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- W4313307614 date "2022-11-16" @default.
- W4313307614 modified "2023-10-02" @default.
- W4313307614 title "Unsupervised Learning Algorithms for Denial of Service Detection in Vehicular Networks" @default.
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- W4313307614 doi "https://doi.org/10.1109/iceccme55909.2022.9987992" @default.
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