Anomaly detection is an important problem that has been researched within diverse
research areas and application domains. Many anomaly detection techniques have been …

Outliers once upon a time regarded as noisy data in statistics, has turned out to be an
important problem which is being researched in diverse fields of research and application …

Markov chain Monte Carlo (MCMC) sampling strategies can be used to simulate hidden
Markov model (HMM) parameters from their posterior distribution given observed data …

Outlier detection has been a very important concept in the realm of data analysis. Recently,
several application domains have realized the direct mapping between outliers in data and …

Masqueraders in computer intrusion detection are people who use somebody else's
computer account. We investigate a number of statistical approaches for detecting …

Time-series of count data are generated in many different contexts, such as web access
logging, freeway traffic monitoring, and security logs associated with buildings. Since this …

This paper presents a series of studies on probabilistic properties of activity data in an
information system for detecting intrusions into the information system. Various probabilistic …

Time series forecasting is a challenging task, where the non-stationary characteristics of
data portray a hard setting for predictive tasks. A common issue is the imbalanced …

This article describes a model based approach to designing network intrusion detection
systems. The article considers general methods applicable to many different types of …

Detecting anomalies in large-scale, streaming datasets has wide applicability in a myriad of
domains like network intrusion detection for cyber-security, fraud detection for credit cards …