Traffic classification acquired the interest of the Internet community early on. Different
approaches have been proposed to classify Internet traffic to manage both security and …

Traffic analysis has many purposes such as evaluating the performance and security of
network operations and management. Therefore, network traffic analysis is considered vital …

Semi-supervised machine learning can be used for obtaining subsets of unlabeled or
partially labeled dataset based on the applicable metrics of dissimilarity. At later stage, the …

In order to study the application of deep learning in the design of network traffic anomaly
detection device, aiming at two common problems in the field of network anomaly detection …

Variable selection (also known as feature selection) is essential to optimize the learning
complexity by prioritizing features, particularly for a massive, high-dimensional dataset like …

Efficient and reliable network operation are the major concerns of computer networks
monitoring, an objective that can be achieved by properly analysing the monitored network …

Network traffic speeds and volumes present practical challenges to analysis. Packet thinning
and flow aggregation protocols provide smaller structured data summaries, but conversely …

The recognition of malware in network traffic is an important research problem. However,
existing solutions addressing this problem rely heavily on the source code and misrecognise …

In the big data network environment, because traditional user abnormal behavior detection
methods cannot meet the needs of massive data detection, it cannot quickly respond to …

Accurately predicting network behavior is beneficial for TCP congestion control, and can
help improve routing, allocating network resources, and optimizing network designs. This …