Mobile encrypted traffic classification using deep learning: Experimental evaluation, lessons learned, and challenges
The massive adoption of hand-held devices has led to the explosion of mobile traffic
volumes traversing home and enterprise networks, as well as the Internet. Traffic …
volumes traversing home and enterprise networks, as well as the Internet. Traffic …
[HTML][HTML] Network traffic classification for data fusion: A survey
Traffic classification groups similar or related traffic data, which is one main stream
technique of data fusion in the field of network management and security. With the rapid …
technique of data fusion in the field of network management and security. With the rapid …
Multi-classification approaches for classifying mobile app traffic
The growing usage of smartphones in everyday life is deeply (and rapidly) changing the
nature of traffic traversing home and enterprise networks, and the Internet. Different tools …
nature of traffic traversing home and enterprise networks, and the Internet. Different tools …
Detection and classification of peer-to-peer traffic: A survey
The emergence of new Internet paradigms has changed the common properties of network
data, increasing the bandwidth consumption and balancing traffic in both directions. These …
data, increasing the bandwidth consumption and balancing traffic in both directions. These …
Ggfast: Automating generation of flexible network traffic classifiers
When employing supervised machine learning to analyze network traffic, the heart of the
task often lies in developing effective features for the ML to leverage. We develop GGFAST …
task often lies in developing effective features for the ML to leverage. We develop GGFAST …
VoIP traffic detection in tunneled and anonymous networks using deep learning
Network management is facing a great challenge to analyze and identify encrypted network
traffic with specific applications and protocols. A significant number of network users …
traffic with specific applications and protocols. A significant number of network users …
A class-oriented feature selection approach for multi-class imbalanced network traffic datasets based on local and global metrics fusion
Feature selection is often used as a pre-processing step for machine learning based
network traffic classification. Many feature selection techniques have been developed to find …
network traffic classification. Many feature selection techniques have been developed to find …
Robust application identification methods for P2P and VoIP traffic classification in backbone networks
T Qin, L Wang, Z Liu, X Guan - Knowledge-Based Systems, 2015 - Elsevier
Application identification plays an essential role in network management such as intrusion
detection and security monitoring. But the continuous growth of bandwidth and massive …
detection and security monitoring. But the continuous growth of bandwidth and massive …
A Feasible and Explainable Network Traffic Classifier Utilizing DistilBERT
While user-oriented service industries are rapidly growing, various network devices provide
these services through different access paths. Accordingly, the network flow is also …
these services through different access paths. Accordingly, the network flow is also …
Mobile app traffic flow feature extraction and selection for improving classification robustness
Z Liu, R Wang, N Japkowicz, Y Cai, D Tang… - Journal of Network and …, 2019 - Elsevier
In machine-learning based mobile app traffic classification, flow feature distributions can
easily drift due to changes in network environments, user habits etc. Unstable features may …
easily drift due to changes in network environments, user habits etc. Unstable features may …