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- W2891855657 abstract "With the frequent use of Wi-Fi and hotspots that provide a wireless Internet environment, awareness and threats to wireless AP (Access Point) security are steadily increasing. Especially when using unauthorized APs in company, government and military facilities, there is a high possibility of being subjected to various viruses and hacking attacks. It is necessary to detect unauthorized Aps for protection of information. In this paper, we use RTT (Round Trip Time) value data set to detect authorized and unauthorized APs in wired / wireless integrated environment, analyze them using machine learning algorithms including SVM (Support Vector Machine), C4.5, KNN (K Nearest Neighbors) and MLP (Multilayer Perceptron). Overall, KNN shows the highest accuracy." @default.
- W2891855657 created "2018-09-27" @default.
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- W2891855657 date "2018-08-01" @default.
- W2891855657 modified "2023-10-14" @default.
- W2891855657 title "Unauthorized Access Point Detection Using Machine Learning Algorithms for Information Protection" @default.
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- W2891855657 doi "https://doi.org/10.1109/trustcom/bigdatase.2018.00284" @default.
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