When network is undergoing problems such as congestion, scan attack, DDoS attack, etc.,
measurements are much more important than usual. In this case, traffic characteristics …

Finding top-k elephant flows is a critical task in network traffic measurement, with many
applications in congestion control, anomaly detection and traffic engineering. As the line …

Approximate stream processing algorithms, such as Count-Min sketch, Space-Saving, etc.,
support numerous applications in databases, storage systems, networking, and other …

Recording the frequency of items in highly skewed data streams is a fundamental and hot
problem in recent years. The literature demonstrates that sketch is the most promising …

Burst is a common pattern in data streams which is characterized by a sudden increase in
terms of arrival rate followed by a sudden decrease. Burst detection has attracted extensive …

Data stream processing plays a critical role in providing fundamental statistics for various
applications, such as anomaly detection. Still, the unbalanced distribution of data streams …

Approximate stream processing has attracted much attention recently. Prior art mostly
focuses on characteristics like frequency, cardinality, and quantile. Persistence, as a new …

This paper concerns the problem of finding persistent items in distributed datasets, which
has many applications such as port scanning and intrusion detection. To the best of our …

Bandwidth testing measures the access bandwidth of end hosts, which is crucial to
emerging Internet applications for network-aware content delivery. However, today's …

Frequent pattern mining is a key area of study that gives insights into the structure and
dynamics of evolving networks, such as social or road networks. However, not only does a …