These datasets can be used for benchmarking unsupervised anomaly detection algorithms (for example "Local Outlier Factor" LOF). The datasets have been obtained from multiple sources and are mainly based on datasets originally used for supervised machine learning. By publishing these modifications, a comparison of different algorithms is now possible for unsupervised anomaly detection
Anomalies in data can be of great importance as they often indicate faulty behaviour. Locating these...
Anomaly detection from Big Cybersecurity Datasets is very important; however, this is a very challen...
Most of the existing methods for anomaly detection use only positive data to learn the data distribu...
Anomaly detection is the process of identifying unexpected items or events in datasets, which differ...
Research in anomaly detection suffers from a lack of realis-tic and publicly-available problem sets....
Outlier Detection is a technique to detect anomalous events or outliers during analysis of the data ...
In data mining, anomaly detection aims at identifying the observations which do not conform to an ex...
Anomaly detection methods are devoted to target detection schemes in which no priori information ab...
Presenting and comparing general anomaly detection algorithms, that do not require task-specific cus...
International audienceNetwork anomalies are unusual traffic mainly induced by network attacks or net...
When sufficient labeled data are available, classical criteria based on Receiver Operating Character...
In today’s world there is lots of data requiring automated processing: nobody can analyze and extrac...
One way to describe anomalies is by saying that anomalies are not concentrated. This leads to the pr...
Detecting anomalies in time series data is important in a variety of fields, including system monito...
A methodology as well as a suggested solution to the problem of unsupervised anomaly detection for c...
Anomalies in data can be of great importance as they often indicate faulty behaviour. Locating these...
Anomaly detection from Big Cybersecurity Datasets is very important; however, this is a very challen...
Most of the existing methods for anomaly detection use only positive data to learn the data distribu...
Anomaly detection is the process of identifying unexpected items or events in datasets, which differ...
Research in anomaly detection suffers from a lack of realis-tic and publicly-available problem sets....
Outlier Detection is a technique to detect anomalous events or outliers during analysis of the data ...
In data mining, anomaly detection aims at identifying the observations which do not conform to an ex...
Anomaly detection methods are devoted to target detection schemes in which no priori information ab...
Presenting and comparing general anomaly detection algorithms, that do not require task-specific cus...
International audienceNetwork anomalies are unusual traffic mainly induced by network attacks or net...
When sufficient labeled data are available, classical criteria based on Receiver Operating Character...
In today’s world there is lots of data requiring automated processing: nobody can analyze and extrac...
One way to describe anomalies is by saying that anomalies are not concentrated. This leads to the pr...
Detecting anomalies in time series data is important in a variety of fields, including system monito...
A methodology as well as a suggested solution to the problem of unsupervised anomaly detection for c...
Anomalies in data can be of great importance as they often indicate faulty behaviour. Locating these...
Anomaly detection from Big Cybersecurity Datasets is very important; however, this is a very challen...
Most of the existing methods for anomaly detection use only positive data to learn the data distribu...
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