Implementation of K-Means Clustering Method for Network Traffic Anomaly Detection

Haeni Budiati (Universitas Kristen Immanuel)
Antonius Bima Murti Wijaya (Universitas Kristen Immanuel)
Barita Suci Vernando Zebua (Universitas Kristen Immanuel)
Jatmika (Universitas Kristen Immanuel)
Yo’el Pieter Sumihar (Universitas Kristen Immanuel)

Jurnal Mantik
Vol. 6 No. 3 (2022): November: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)

Anomalies may degrade network performance for specific network traffic. Because of its nature, it causes abnormal network traffic. Using the K-means clustering method, this study addresses the formulation of the problem of detecting network bandwidth usage anomalies. The objective of this study is to identify potential network traffic anomalies. This study uses the K-Means Method to predict the value of the network traffic anomalies that will appear. K-Means operates by repeatedly iterating based on the initial cluster entered, until the same cluster results are discovered. The results of the study indicate that predicting the occurrence of anomalies with K-Means will help suppress activities that impede network traffic.