Classification of Network Traffic through Network Flow Using Machine Learning Algorithms to Anomaly Detection

• Welton T. M. Sousa IFMG
• Carlos A. Silva IFMG

DOI: https://doi.org/10.5753/encompif.2022.223182

Abstract

The classification of network traffic through the network flow holds great in the context of cyber attacks. Several segments of society are for these efforts, which represent significant financial. Efficient methods such as preventive and firewall not always identify anomalies in the network. Reason for these circumstances, this article proposes the study and application of seven machine learning algorithms with the use of feature selection of network application traffic, prioritizing automation in anomalous flow identification, contributing to obtain metrics helping decision making by network administrators.

Keywords: Machine Learning, Intrusion Detection, UNSW-NB15, Cybersecurity, Network Traffic Analysis

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