Detection of Network Anomalies with Machine Learning Methods
10th International Symposium on Digital Forensics and Security, ISDFS 2022, İstanbul, Türkiye, 6 - 07 Haziran 2022, (Tam Metin Bildiri)
- Yayın Türü: Bildiri / Tam Metin Bildiri
- Doi Numarası: 10.1109/isdfs55398.2022.9800814
- Basıldığı Şehir: İstanbul
- Basıldığı Ülke: Türkiye
- Anahtar Kelimeler: Cyber Attack Detection, K-Nearest Neighbor Algorithm, Naive Bayes Theorem, Supervised Learning, UNSW-NB15 dataset
- Maltepe Üniversitesi Adresli: Evet
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.