Offline/realtime traffic classification using semi-supervised learning

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Hacking smart machines with smarter ones: How to extract meaningful data from machine learning classifiers (2013, methodology): “, [27, 54, 49, 23, 25]) and machine learning algorithms pervade several contexts of information technology.”
Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges (2017, background): “developments include a hybrid model to classify traffic in more unsupervised manner [173], which uses both labeled and unlabeled data to train the classifier making it more durable and efficient.”
A comprehensive survey on machine learning for networking: evolution, applications and research opportunities (2018, methodology): “However, it may be possible to overcome this by iterative retraining of the classifier, either by using approach similar to [137] or introducing rules (e.”
Network Traffic Classification Using Correlation Information (2013, background): “5 [11], BayesNet [11], and Erman’s clustering-based method [10].”
State-of-the-Art Deep Learning: Evolving Machine Intelligence Toward Tomorrow’s Intelligent Network Traffic Control Systems (2017, unknown): “[180].”