Related papers: Data Augmentation for Traffic Classification

Data Augmentation for Traffic Classification

URL: http://arxiv.org/abs/2401.10754v2
Date: Tue, 23 Jan 2024 12:53:33 GMT
Title: Data Augmentation for Traffic Classification
Authors: Chao Wang, Alessandro Finamore, Pietro Michiardi, Massimo Gallo, Dario Rossi
Abstract summary: Data Augmentation (DA) is a technique widely adopted in Computer Vision (CV) and Natural Language Processing (NLP) tasks. DA has struggled to gain traction in networking contexts, particularly in Traffic Classification (TC) tasks.
Score: 54.92823760790628
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Data Augmentation (DA) -- enriching training data by adding synthetic samples -- is a technique widely adopted in Computer Vision (CV) and Natural Language Processing (NLP) tasks to improve models performance. Yet, DA has struggled to gain traction in networking contexts, particularly in Traffic Classification (TC) tasks. In this work, we fulfill this gap by benchmarking 18 augmentation functions applied to 3 TC datasets using packet time series as input representation and considering a variety of training conditions. Our results show that (i) DA can reap benefits previously unexplored, (ii) augmentations acting on time series sequence order and masking are better suited for TC than amplitude augmentations and (iii) basic models latent space analysis can help understanding the positive/negative effects of augmentations on classification performance.

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