A benchmarking study on stream network traffic analysis using active learning

Abstract

Analyzing network activity as it occurs is an important task since it allows for the prevention of malicious activity on the host system and the network. In this work, we investigate the performance of different budgeting strategies, as well as an adaptive Artificial Neural Network to analyze the activities on streaming network traffic. Our results show that all of our budgeting strategies (with the exception of the fixed uncertainty strategy) are suitable candidates for classification of streaming network traffic where some of the state-of-the-art classifiers achieved accuracies in the range of 90% or higher.