Many real-world Internet of Things (IoT) systems, which include a variety of Internet-
connected sensory devices, produce substantial amounts of multivariate time-series data …

Anomaly detection on multivariate time-series is of great importance in both data mining
research and industrial applications. Recent approaches have achieved significant progress …

Large companies need to monitor various metrics (for example, Page Views and Revenue)
of their applications and services in real time. At Microsoft, we develop a time-series …

The anomalies of microservice invocation traces (traces) often indicate that the quality of the
microservice-based large software service is being impaired. However, timely and …

Networks and network analysis are arguably one of the largest growth areas of the early
twenty-first century in the quantitative sciences. Despite roots in social network analysis …

We explore the nature of traffic in data centers, designed to support the mining of massive
data sets. We instrument the servers to collect socket-level logs, with negligible performance …

Closely monitoring service performance and detecting anomalies are critical for Internet-
based services. However, even though dozens of anomaly detectors have been proposed …

The detection of outliers in spatio-temporal traffic data is an important research problem in
the data mining and knowledge discovery community. However to the best of our …

Statistical machine learning techniques have recently garnered increased popularity as a
means to improve network design and security. For intrusion detection, such methods build …

Detecting anomalous traffic is a crucial part of managing IP networks. In recent years,
network-wide anomaly detection based on Principal Component Analysis (PCA) has …