Performance Evaluation of Machine Learning Nave Bayes Algorithms for Network Traffic Classification

Abstract

Network Traffic Classification (NTC) is an important field for different network statistics like management, malware detection and other paramount constraints. Artificial Intelligence (AI) including Machine Learning (ML) and Deep Learning (DL), on the other hand, plays a very important field nowadays due to its significant capabilities with an extremely different fields and for complex problems. ML, specifically, provides tools in the most important network fields like traffic management, security, etc. Performance evaluation is a very important aspect of any system. This research paper provides a method for NTC using ML Nave Bayes (NB) algorithm in terms of Bernoulli, Multinominal and Gaussian for classifying captured network traffic in two different datasets and perform a performance evaluation and a comparison among these algorithms. The first dataset is a VPN-nonVPN (ISCXVPN2016) dataset while the second is a packet-captured regular Wi-Fi traffic flow dataset for video browsing on the web. Results were comparable in terms of f1-score, accuracy and processing time. Bernoulli NB provides average 93.05% accuracy with 742 ms, Multinominal NB provides average 98.78% accuracy with 78.3 ms processing time and finally, Gaussian NB provides average 69.14% with 46.85 ms processing time.