The recent development of smart devices has lead to an explosion in data generation and
heterogeneity. Hence, current networks should evolve to become more intelligent, efficient …

In the diagnosis and treatment of cancer, cancer classification is a vital issue. Gene selection
is much needed to solve the high dimensionality issue in microarray data, small sample size …

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 …

With the rapid development of communication network technology, the types and quantity of
network traffic data are accordingly increasing. Network traffic classification has become a …

As the majority of Internet traffic is encrypted by the Transport Layer Security (TLS) protocol,
recent advances leverage Deep Learning (DL) models to conduct encrypted traffic …

With the increasing demand for the protection of personal network meta-data, encrypted
networks have grown in popularity, so do the challenge of monitoring and analyzing …

The rapid development of smart devices triggers a surge in new traffic and applications.
Thus, network traffic classification has become a challenge in modern communications and …

The classification of network traffic, which involves classifying and identifying the type of
network traffic, is the most fundamental step to network service improvement and modern …

Network Traffic Classification enables a number of practical applications ranging from
network monitoring to resource management, with security implications as well. Nowadays …

Network intrusion detection systems (NIDS) are critical to defending network systems from
cyber attacks. Recently, machine learning has been applied to enhance NIDS capability. To …