Anomaly Detection In Network Traffic Using Machine Learning

Year 2023, Volume: 2 Issue: 3, 5 - 12, 31.12.2023

Roaa Mohammed , Fatih Akay

Abstract

The primary theme of this paper revolves around the detection of anomalies and measurement of device health, using a Key Performance Indicator (KPI) dataset spanning twenty-one days. The study aims to improve the accuracy of models through the utilization of machine learning (ML) methods. The accuracy of each model was measured using a confusion matrix, and the results indicate that deep learning methods outperform classification methods across all models. Overall, this study provides valuable insights into the use of ML methods for improving the accuracy of anomaly detection and device health measurement in KPI datasets, with potential applications in various fields.

Keywords

Anomaly detection, Machine learning (ML), Key Performance Indicator (KPI)

Supporting Institution

FEN BİLİMLERİ ENSTİTÜSÜ, CU

Project Number

750785

Thanks

I would like to thank my supervisor, Prof. Dr. Mehmet Fatih AKAY, Also, like to thank all my professors at Çukurova University-Adana.

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