The sharp increase in encrypted traffic brings a huge challenge to traditional traffic
classification methods. Combining deep learning with time series analysis techniques is a …

Classifying encrypted traffic from emerging applications is important but challenging as
many conventional traffic classification approaches are ineffective, thus calling for novel …

The increase in the source and size of encrypted network traffic brings significant challenges
for network traffic analysis. The challenging problem in the encrypted traffic classification …

The increasing volume of encrypted traffic from emerging applications has made
conventional traffic classification approaches ineffective and called for novel methods for …

With more attention paid to user privacy and communication security, the volume of
encrypted traffic rises sharply, which brings a huge challenge to traditional rule-based traffic …

With the rapid development of network technology and encryption technology, network
security issues have received more and more attention, and network encryption traffic is …

In recent years, deep learning-based encrypted traffic classification has proven to be
effective; especially, using neural networks to extract features from raw traffic to classify …

As the size and source of network traffic increase, so does the challenge of monitoring and
analyzing network traffic. The challenging problems of classifying encrypted traffic are the …

Traffic classification associates packet streams with known application labels, which is vital
for network security and network management. With the rise of NAT, port dynamics, and …

With the rapidly emerging encryption techniques for network traffic, the classification of
encrypted traffic has increasingly become significantly important in network management …