Detection of Network Anomalies with Machine Learning Methods
Küçük Resim Yok
Tarih
2022
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Institute of Electrical and Electronics Engineers Inc.
Erişim Hakkı
info:eu-repo/semantics/closedAccess
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
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
