Main Article Content

Haeni Budiati
Antonius Bima Murti Wijaya
Barita Suci Vernando Zebua
Jatmika
Yo’el Pieter Sumihar

Abstract

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.

Downloads

Download data is not yet available.

Article Details

How to Cite
Budiati, H., Wijaya, A. B. M. ., Zebua, B. S. V., Jatmika and Sumihar, Y. P. . (2022) “Implementation of K-Means Clustering Method for Network Traffic Anomaly Detection”, Jurnal Mantik, 6(3), pp. 3499-3504. Available at: https://iocscience.org/ejournal/index.php/mantik/article/view/3218 (Accessed: 29March2024).
References
Aini, F. D., Riadi, I., & Umar, R. (2018). Perancangan Deteksi Anomali Traffic Untuk Investigasi Log Menggunakan Metode K-Means Clusters. Prosiding SNST Fakultas Teknik, 1(1).
Ananto, R. P., Purwanto, Y., & Novianty, A. (2017). Deteksi Jenis Serangan Pada Distributed Denial Of Service Berbasis Clustering dan Classification Menggunakan Algoritma Minkowski Weighted K-Means dan Decision Tree. EProceedings of Engineering, 4(1).
Chakraborty, N. (2013). Intrusion detection system and intrusion prevention system: A comparative study. International Journal of Computing and Business Research (IJCBR), 4(2), 1–8.
Chandel, S. K. (2017). Intrusion Detection System using K-Means Data Mining and Outlier Detection Approach. Bangalore: Faculty of Informatics, Masaryk University.
Fink, G. A., Chappell, B. L., Turner, T. G., & O’Donoghue, K. F. (2002). A metrics-based approach to intrusion detection system evaluation for distributed real-time systems. Proceedings 16th International Parallel and Distributed Processing Symposium, 8-pp.
Gopi, E. S. (2007). Algorithm collections for digital signal processing applications using Matlab. Springer Science & Business Media.
Harjono, H., & Wicaksono, A. P. (2013). Honeyd untuk Mendeteksi Serangan Jaringan di Universitas Muhammadiyah Purwokerto. JUITA: Jurnal Informatika, 2(4).
Hermanto, T. I. (n.d.). Implementasi Algoritma Association Rule Dan K-Means Sebagai Sistem Rekomendasi Produk Pada Website Penjualan Online. Stt-Wastukancana. Ac. Id, 70–73.
Lubis, A. H. (2016). Model segmentasi pelanggan dengan kernel k-means clustering berbasis customer relationship management. Sinkron: Jurnal Dan Penelitian Teknik Informatika, 1(1).
Putra, I. W. O. K., Purwanto, Y., & Suratman, F. Y. (2015). Perancangan dan Analisis Deteksi Anomaly Berbasis Clustering Menggunakan Algoritma Modified K-Means dengan Timestamp Initialization pada Sliding Window. EProceedings of Engineering, 2(2).
Ridho, F., & Kusuma, A. A. (2018). Deteksi Intrusi Jaringan dengan K-Means Clustering pada Akses Log dengan Teknik Pengolahan Big Data. Jurnal Aplikasi Statistika & Komputasi Statistik, 10(1), 53–66.
Rosa, S. L. (2018). Pendeteksian Anomali Penggunaan Internet di LAN Universitas Islam Riau Indonesia. IT Journal Research and Development, 3(1), 72–83.
Theodoridis, S., & Koutroumbas, K. (2006). Pattern recognition. Elsevier.
Wulandari, G. F. (2014). Segmantasi Pelanggan Menggunakan Algoritma K-Means Untuk Customer Relationship Management (CRM) Pada Hijab Miulan. Ind. Mark. Manag, 1, 7.
Zulfadhilah, M., Riadi, I., & Prayudi, Y. (2016). Log classification using K-means clustering for identify Internet user behaviors. International Journal Of Computer Applications, 154(3).