End-to-end encrypted traffic classification with one-dimensional convolution neural networks

By Wei Wang, Ming Zhu, Jinlin Wang, Xuewen Zeng, Zhongzhen Yang. Intelligence and Security Informatics, 2017

825 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Deep Learning for Encrypted Traffic Classification: An Overview (2018, methodology): “The simplest convolutional neural network (CNN) model is proposed in [7] which basically represent each flow or session with a 1-dimensional vector to feed the CNN model.”
A Survey of Deep Learning Methods for Cyber Security (2019, methodology): “[163] 9 Traffic Identification ISCX VPN–non-VPN traffic dataset [164]”
Machine Learning and Deep Learning Methods for Cybersecurity (2018, neutral): “[77] propose a one-dimensional convolutional”
Deep packet: a novel approach for encrypted traffic classification using deep learning (2017, methodology): “proposed a similar approach to Deep Packet for traffic characterization on “ISCX VPN-nonVPN” traffic dataset [44].”
Deep Learning in Mobile and Wireless Networking: A Survey (2018, methodology): “Work in [198] proposes to use a 1D CNN for encrypted traffic classification.”
Mobile Encrypted Traffic Classification Using Deep Learning: Experimental Evaluation, Lessons Learned, and Challenges (2019, methodology): “It is worth noticing that all the works approaching the TC using DL [19]–[21], [23] considered either flows or biflows as”
A Survey on Machine Learning Techniques for Cyber Security in the Last Decade (2020, methodology): “CNN has also been used widely for the classiﬁcation of malicious trafﬁc [247]–[249].”