In this Chapter we give an overview of statistical methods for anomaly detection (AD), thereby targeting an audience of practitioners with general knowledge of statistics. We focus on the applicability of the methods by stating and comparing the conditions in which they can be applied and by discussing the parameters that need to be set
In today’s world there is lots of data requiring automated processing: nobody can analyze and extrac...
The impact of an anomaly is domain-dependent. In a dataset of network activities, an anomaly can imp...
Anomaly detection is the problem of identifying data points or patterns that do not conform to norma...
In this Chapter we give an overview of statistical methods for anomaly detection (AD), thereby targe...
In this Chapter we give an overview of statistical methods for anomaly detection (AD), thereby targe...
In this article we review different approaches to the anomaly detection problems, their applications...
This paper contains review of algorithms, methods and tools nowadays used for anomaly detection.Anom...
International audienceData mining has become an important task for researchers in the past few years...
In this chapter, the algorithm summary of the proposed autonomous anomaly detection (AAD) algorithm ...
This survey defines the problem of anomaly detection and provides an overview of existing methods. T...
This work in progress outlines a comparison of anomaly detection methods that we are undertaking. We...
In this chapter, the empirical approach to the problem of anomaly detection is presented, which is f...
Anomaly analysis is of great interest to diverse fields, including data mining and machine learning,...
In this work we approach the problem of deploying anomaly detection techniques for detecting cyber a...
AbstractIn the present world huge amounts of data are stored and transferred from one location to an...
In today’s world there is lots of data requiring automated processing: nobody can analyze and extrac...
The impact of an anomaly is domain-dependent. In a dataset of network activities, an anomaly can imp...
Anomaly detection is the problem of identifying data points or patterns that do not conform to norma...
In this Chapter we give an overview of statistical methods for anomaly detection (AD), thereby targe...
In this Chapter we give an overview of statistical methods for anomaly detection (AD), thereby targe...
In this article we review different approaches to the anomaly detection problems, their applications...
This paper contains review of algorithms, methods and tools nowadays used for anomaly detection.Anom...
International audienceData mining has become an important task for researchers in the past few years...
In this chapter, the algorithm summary of the proposed autonomous anomaly detection (AAD) algorithm ...
This survey defines the problem of anomaly detection and provides an overview of existing methods. T...
This work in progress outlines a comparison of anomaly detection methods that we are undertaking. We...
In this chapter, the empirical approach to the problem of anomaly detection is presented, which is f...
Anomaly analysis is of great interest to diverse fields, including data mining and machine learning,...
In this work we approach the problem of deploying anomaly detection techniques for detecting cyber a...
AbstractIn the present world huge amounts of data are stored and transferred from one location to an...
In today’s world there is lots of data requiring automated processing: nobody can analyze and extrac...
The impact of an anomaly is domain-dependent. In a dataset of network activities, an anomaly can imp...
Anomaly detection is the problem of identifying data points or patterns that do not conform to norma...