• Netravati Patil
• Pavitra Gadhar

TensorFlow is an interface for expressing machine learning algorithms and an implementation for executing such algorithms. Applications are becoming more complicated and diverse as the network environment grows day by day. So, it is important to classify application traffic accurately. Although there are many ways to classify applications traffic, machine learning based approaches are becoming more efficient in nowadays. This is because machine learning methods are more appropriate than existing methods for accurate and efficient applications traffic classification. Payload signature methods have limitations to deal with various patterns and increasing application traffic complexity. In this paper, we propose a method for extracting flow features and a system for classifying applications traffic based on Machine Learning. This paper describes the TensorFlow interface and an implementation of that interface that we have built at Google. The TensorFlow API and a reference implementation were released as an open-source package.

Traffic Classification, Machine Learning, Learning Feature, Flow Feature Extraction, Normalization

"Application traffic using tensorflow machine learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 2, page no.348-354, February-2019, Available :http://www.jetir.org/papers/JETIR1902B48.pdf

"Application traffic using tensorflow machine learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 2, page no. pp348-354, February-2019, Available at : http://www.jetir.org/papers/JETIR1902B48.pdf