Cloud computing represents a new paradigm where computing resources are offered as services in the world via communication Internet. As many new types of attacks are arising at a high frequency, the cloud computing services are exposed to an increasing amount of security threats. To reduce security risks, two approaches of the network traffic anomaly detection in cloud communications have been ...

Currently, time series anomaly detection is attracting significant interest. This is especially true in industry, where companies continuously monitor all aspects of production processes using various sensors. In this context, methods that automatically detect anomalous behavior in the collected data could have a large impact. Unfortunately, for a variety of reasons, it is often difficult to co...

A new anomaly detection scheme based on growing structure multiple model system (GSMMS) is proposed in this paper to detect and quantify the effects of anomalies. The GSMMS algorithm combines the advantages of growing self-organizing networks with efficient local model parameter estimation into an integrated framework for modeling and identification of general nonlinear dynamic systems. The ide...

The main objective of this article is to develop scalable dynamic anomaly detectors with high-fidelity simulators power systems. On the one hand, models in are typically “intractable” if opts describe them a mathematical formulation order apply existing model-based approaches from detection literature. other pure data-driven methods developed primarily machine learning literature neglect our kn...

Anomaly detection can infer the presence of errors without observing target services, but detecting variations in observable parts system on which services reside. This is a promising technique complex software-intensive systems, because either instrumenting services' internals exceedingly time-consuming, or encapsulation makes them not accessible. Unfortunately, such systems anomaly often inef...

Anomaly detection refers to the problem of finding patterns in data that do not conform to expected normal behavior. Anomaly detection is extensively used in a wide variety of applications such as monitoring business news, epidemic or bioterrorism detection, intrusion detection, hardware fault detection, network alarm monitoring, and fraud detection [13]. Anomaly detection in target tracking is...