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Network traffic data is huge, varying and imbalanced because various classesare not equally distributed. Machine learning (ML) algorithms for trafficanalysis uses the samples from this data to recommend the actions to be takenby the network administrators as well as training. Due to imbalances indataset, it is difficult to train machine learning algorithms for trafficanalysis and these may give biased or false results leading to seriousdegradation in performance of these algorithms. Various techniques can beapplied during sampling to minimize the effect of imbalanced instances. In thispaper various sampling techniques have been analysed in order to compare thedecrease in variation in imbalances of network traffic datasets sampled forthese algorithms. Various parameters like missing classes in samples,probability of sampling of the different instances have been considered forcomparison.

Sampling Based Approaches to Handle Imbalances in Network Traffic Dataset for Machine Learning Techniques