Edge networking brings computation and data storage as close to the point of request as
possible. Various intelligent devices are connected to the edge nodes where traffic packets …

Semi-Supervised Learning (SSL) is an influential derivative that allows humans to uncover
invisible knowledge, potentially substituting it for extensive labeling data. Despite the …

Traffic Classification (TC) has been applied to a wide range of applications, from security
monitoring to quality of service (QoS) provisioning in network Internet Service Providers …

Encrypted network traffic classification is an essential task in modern communications, which
is used in a wide range of applications, such as network resource allocation, QoS (Quality of …

A variety of model representation methods have been used in recent works to translate
machine learning models into programmable switch rules to address network classification …

Network traffic classification is a key component for network management, Quality-of-Service
management, as well as for network security. Therefore, developing machine learning (ML) …

Widespread use of cloud computing resources calls for reliable network connections, while
anomalies in network traffic impact the availability of cloud resources in a negative way …

The use of Machine Learning for anomaly detection in cyber security-critical applications,
such as intrusion detection systems, has been hindered by the lack of explainability. Without …

Nowadays, Internet users are rising and need to be supplied with an adoptable quality of
service (QoS). Network traffic classification is one of the essential functions that can lead the …

As a new network paradigm, Software-defined networking (SDN) realizes centralized
management of the network by separating the control plane and the data plane. While SDN …