Attributes | Values |
---|
rdf:type
| |
rdfs:seeAlso
| |
Description
| - Many contemporary domains, e.g. network intrusion detection, fraud detection, etc., call for an anomaly detector processing a continuous stream of data. This need is driven by the high rate of their acquisition, by limited resources for storing them, or by privacy issues. The data can be also non-stationary requiring the detector to continuously adapt to their change. A good detector for these domains should therefore have a low training and classification complexity, on-line training algorithm, and, of course, a good detection accuracy. This paper proposes a detector trying to meet all these criteria. The detector consists of multiple weak detectors, each implemented as a one dimensional histogram. The one-dimensional histogram was chosen because it can be efficiently created on-line, and probability estimates can be efficiently retrieved from it. This construction gives the detector linear complexity of training and classification with respect to the input dimension, number of samples, and number of weak detectors. The accuracy of the detector is compared to seven anomaly detectors from the prior art on the range of 36 classification problems from UCI database. Results show that despite detector's simplicity, its accuracy is competitive to that of more complex detectors with a substantially higher computational complexity.
- Many contemporary domains, e.g. network intrusion detection, fraud detection, etc., call for an anomaly detector processing a continuous stream of data. This need is driven by the high rate of their acquisition, by limited resources for storing them, or by privacy issues. The data can be also non-stationary requiring the detector to continuously adapt to their change. A good detector for these domains should therefore have a low training and classification complexity, on-line training algorithm, and, of course, a good detection accuracy. This paper proposes a detector trying to meet all these criteria. The detector consists of multiple weak detectors, each implemented as a one dimensional histogram. The one-dimensional histogram was chosen because it can be efficiently created on-line, and probability estimates can be efficiently retrieved from it. This construction gives the detector linear complexity of training and classification with respect to the input dimension, number of samples, and number of weak detectors. The accuracy of the detector is compared to seven anomaly detectors from the prior art on the range of 36 classification problems from UCI database. Results show that despite detector's simplicity, its accuracy is competitive to that of more complex detectors with a substantially higher computational complexity. (en)
|
Title
| - Anomaly detection by bagging
- Anomaly detection by bagging (en)
|
skos:prefLabel
| - Anomaly detection by bagging
- Anomaly detection by bagging (en)
|
skos:notation
| - RIV/68407700:21230/13:00211170!RIV14-GA0-21230___
|
http://linked.open...avai/predkladatel
| |
http://linked.open...avai/riv/aktivita
| |
http://linked.open...avai/riv/aktivity
| |
http://linked.open...vai/riv/dodaniDat
| |
http://linked.open...aciTvurceVysledku
| |
http://linked.open.../riv/druhVysledku
| |
http://linked.open...iv/duvernostUdaju
| |
http://linked.open...titaPredkladatele
| |
http://linked.open...dnocenehoVysledku
| |
http://linked.open...ai/riv/idVysledku
| - RIV/68407700:21230/13:00211170
|
http://linked.open...riv/jazykVysledku
| |
http://linked.open.../riv/klicovaSlova
| - anomaly detection; unsupervised learning; big data (en)
|
http://linked.open.../riv/klicoveSlovo
| |
http://linked.open...ontrolniKodProRIV
| |
http://linked.open...in/vavai/riv/obor
| |
http://linked.open...ichTvurcuVysledku
| |
http://linked.open...cetTvurcuVysledku
| |
http://linked.open...vavai/riv/projekt
| |
http://linked.open...UplatneniVysledku
| |
http://linked.open...iv/tvurceVysledku
| |
http://localhost/t...ganizacniJednotka
| |