Issues and future directions in traffic classification
By Alberto Dainotti, Antonio Pescapè, K. Claffy. IEEE Network, 2012
639 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Networks and Communications, Signal Processing
View Paper: https://www.semanticscholar.org/paper/e5b96ed888218ba58d3d9ef6e920569cfb76ce4d
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Arash Habibi Lashkari
- Ali Akbar Ghorbani
- Wei Wang
- Xuewen Zeng
- Mohammad Ali Salahuddin
- Mohammad Lotfollahi
- Sara Ayoubi
- Raouf Boutaba
In Computer Networks and Communications
- Arash Habibi Lashkari
- Ali Akbar Ghorbani
- Giuseppe Aceto
- Antonio Pescapè
- Wei Wang
- Xuewen Zeng
- Mohammad Ali Salahuddin
- Mohammad Lotfollahi
In Signal Processing
- Ali Akbar Ghorbani
- Domenico Ciuonzo
- Arash Habibi Lashkari
- Antonio Pescapè
- Antonio Montieri
- Giuseppe Aceto
- Xin Liu
- Shahbaz Rezaei
Citations in the corpus
- End-to-end encrypted traffic classification with one-dimensional convolution neural networks (2017, background): “The most common two choices of traffic representation are session and flow [18].”
- Towards the Deployment of Machine Learning Solutions in Network Traffic Classification: A Systematic Survey (2019, neutral): “A more recent review, presented in [3],”
- A comprehensive survey on machine learning for networking: evolution, applications and research opportunities (2018, background): “Portbased techniques are unreliable and antiquated, largely due to the use of dynamic port negotiation, tunneling and misuse of port numbers assigned to well-known applications for obfuscating traffic and avoiding firewalls [54, 109, 176, 286].”
- Characterization of Tor Traffic using Time based Features (2017, background): “However, the continuous growth of Internet and its offer of services, along with the latest trend to encrypt and/or disguise these services, makes traffic classification a great challenge for the Internet research community (Dainotti et al., 2012).”
- Malware traffic classification using convolutional neural network for representation learning (2017, background): “A. DataSet As Dainotti et al. [13] pointed out, lack of a variety of sharable traces dataset as test data is the most obvious obstacle to progress on traffic classification.”
- Mobile Encrypted Traffic Classification Using Deep Learning: Experimental Evaluation, Lessons Learned, and Challenges (2019, methodology): “common performance measures [1]: accuracy (the fraction of correctly classified instances), precision (prec, i.”
- Deep packet: a novel approach for encrypted traffic classification using deep learning (2017, methodology): “, see [13], [15], [42] and the references therein).”