Abstract Machine Learning (ML) algorithms have been increasingly replacing people in
several application domains—in which the majority suffer from data imbalance. In order to …

The rapid adoption of mobile devices has dramatically changed the access to various
networking services and led to the explosion of mobile service traffic. Mobile service traffic …

With the popularity of Deep Learning (DL), researchers have begun to apply DL to tackle
with encrypted traffic classification problems. Although these methods can automatically …

The complex problems of multiclass imbalance, virtual or real concept drift, concept
evolution, high-speed traffic streams and limited label cost budgets pose severe challenges …

Encrypted traffic classification is of great significance for advanced network services. Though
encryption methods seem unbroken in protecting users' privacy, existing studies have …

With the proliferation of encrypted traffic, machine learning (ML) based network traffic
classification (NTC) has become the mainstream method. However, most studies ignored …

Traffic data contains deep domain-specific knowledge, making labeling challenging, and the
lack of labeled data adversely impacts the accuracy of learning-based traffic analysis. The …

It is crucial to accurately identify the type of traffic and application so that it can enable
various policy-driven network management and security monitoring. However, with the …

Network traffic classification plays an important role in various network functions such as
network security issues and network management. In addition to port-based and payload …

With the rapid development of network technology, the Internet has accelerated the
generation of network traffic, which has made network security a top priority. In recent years …