Discriminators for use in flow-based classification
By Andrew William Moore, Denis Zuev, Michael Crogan. 2013
379 citations recorded by the source corpus.
View Paper: https://www.semanticscholar.org/paper/68919d1eaf5708163da8172691a8bec25b64105b
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Fannia Pacheco
- Mohammad Lotfollahi
- Ernesto Exposito
- Mahdi Jafari Siavoshani
- Mathieu Gineste
- Ramin Shirali Hossein Zade
- Cédric Baudoin
- Mohammdsadegh Saberian
Citations in the corpus
- A survey of methods for encrypted traffic classification and analysis (2015, background): “[43], which can be used to classify flow records.”
- Deep packet: a novel approach for encrypted traffic classification using deep learning (2017, methodology): “manually selected 111 flow features described in [29] and achieved 94% of accuracy for 14 class of applications using k-NN algorithm [47].”
- Towards the Deployment of Machine Learning Solutions in Network Traffic Classification: A Systematic Survey (2019, methodology): “Under this assumption, the work in [94] proposes 249 statistical features, which can be extracted from flow network traffic.”
- Network Traffic Classifier With Convolutional and Recurrent Neural Networks for Internet of Things (2017, methodology): “The method is applied to a one-to-one combination of classes with a dataset provided by the University of Cambridge (Moore dataset) [11].”