Detection of network anomalies with machine learning methods

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.