Detection of network anomalies with machine learning methods
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Tarih
2022
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Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
The present study, aimed to detect cyber-attacks, and unexpected access requests on devices in the telecommunication networks, enabling the necessary measures to be taken early. With K-Nearest Neighbors (KNN) and Naive Bayes machine learning methods, predicted whether the raw data packets contain cyber-attack according to different properties of these packets using the UNSW-NB15 dataset. KNN algorithms with different K values and the Naive Bayes method were compared according to accuracy rates and the results were given in the table. As a result, changes in accuracy rates were observed according to different k neighbor values in the KNN algorithm. Higher accuracy rates than Naive Bayes were achieved in the models created with the KNN algorithm.
Açıklama
Anahtar Kelimeler
Cyber attack detection, Supervised learning, KNearest neighbor algorithm, Naive bayes theorem, UNSWNB15 dataset
Kaynak
10th International Symposium on Digital Forensics and Security (ISDFS)
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Sayı
Künye
Kara, İ.R. and Varol, A. (2022). Detection of network anomalies with machine learning methods. 10th International Symposium on Digital Forensics and Security (ISDFS), s.1-6.
