Detection of Network Anomalies with Machine Learning Methods

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Tarih

2022

Dergi Başlığı

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Yayıncı

Institute of Electrical and Electronics Engineers Inc.

Erişim Hakkı

info:eu-repo/semantics/closedAccess

Araştırma projeleri

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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. © 2022 IEEE.

Açıklama

IEEE Society
10th International Symposium on Digital Forensics and Security, ISDFS 2022 -- 6 June 2022 through 7 June 2022 -- -- 180285

Anahtar Kelimeler

Cyber Attack Detection, K-Nearest Neighbor Algorithm, Naive Bayes Theorem, Supervised Learning, Unsw-Nb15 Dataset

Kaynak

10th International Symposium on Digital Forensics and Security, ISDFS 2022

WoS Q Değeri

Scopus Q Değeri

N/A

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