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Data Augmentation for Traffic Classification
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.
Toward Generative Data Augmentation for Traffic Classification
Data Augmentation (DA)-augmenting training data with synthetic samples-is wildly adopted in Computer Vision (CV) to improve models performance.
"It's a Match!" -- A Benchmark of Task Affinity Scores for Joint Learning
Some tasks may benefit from being learned together while others may be detrimental to one another.
Accelerating Deep Learning Classification with Error-controlled Approximate-key Caching
While Deep Learning (DL) technologies are a promising tool to solve networking problems that map to classification tasks, their computational complexity is still too high with respect to real-time traffic measurements requirements.
FENXI: Deep-learning Traffic Analytics at the Edge
The design of FENXI decouples forwarding operations and traffic analytics which operates at different granularities i. e., packet and flow levels.
