Detailed Information on Publication Record

@article{18946,
   author = {Velan, Petr and Čermák, Milan and Čeleda, Pavel and Drašar, Martin},
   article_number = {5},
   keywords = {encrypted traffic; monitoring; network; traffic classification; traffic analysis; machine learning; encryption protocols},
   language = {eng},
   issn = {1055-7148},
   journal = {International Journal of Network Management},
   title = {A Survey of Methods for Encrypted Traffic Classification and Analysis},
   url = {http://onlinelibrary.wiley.com/doi/10.1002/nem.1901/epdf},
   volume = {25},
   year = {2015}
}

TY  - JOUR
ID  - 18946
AU  - Velan, Petr - Čermák, Milan - Čeleda, Pavel - Drašar, Martin
PY  - 2015
TI  - A Survey of Methods for Encrypted Traffic Classification and Analysis
JF  - International Journal of Network Management
VL  - 25
IS  - 5
SP  - 355-374
EP  - 355-374
PB  - Sons, Ltd.
SN  - 1055-7148
KW  - encrypted traffic
KW  - monitoring
KW  - network
KW  - traffic classification
KW  - traffic analysis
KW  - machine learning
KW  - encryption protocols
UR  - http://onlinelibrary.wiley.com/doi/10.1002/nem.1901/epdf
N2  - With the widespread use of encrypted data transport network traffic encryption is becoming a standard nowadays. This presents a challenge for traffic measurement, especially for analysis and anomaly detection methods which are dependent on the type of network traffic. In this paper, we survey existing approaches for classification and analysis of encrypted traffic. First, we describe the most widespread encryption protocols used throughout the Internet. We show that the initiation of an encrypted connection and the protocol structure give away a lot of information for encrypted traffic classification and analysis. Then, we survey payload and feature-based classification methods for encrypted traffic and categorize them using an established taxonomy. The advantage of some of described classification methods is the ability to recognize the encrypted application protocol in addition to the encryption protocol. Finally, we make a comprehensive comparison of the surveyed feature-based classification methods and present their weaknesses and strengths.
ER  - 

VELAN, Petr, Milan ČERMÁK, Pavel ČELEDA and Martin DRAŠAR. A Survey of Methods for Encrypted Traffic Classification and Analysis. \textit{International Journal of Network Management}. Sons, Ltd., vol.~25, No~5, p.~355-374. ISSN~1055-7148. 2015.