Anomaly detection by bagging

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F13%3A00211170" target="_blank" >RIV/68407700:21230/13:00211170 - isvavai.cz</a>

Výsledek na webu

<a href="http://ama.imag.fr/COPEM/copem2013_proceedings.pdf" target="_blank" >http://ama.imag.fr/COPEM/copem2013_proceedings.pdf</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Anomaly detection by bagging

Popis výsledku v původním jazyce

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, orby 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, ofcourse, 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 sample

Název v anglickém jazyce

Anomaly detection by bagging

Popis výsledku anglicky

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, orby 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, ofcourse, 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 sample

Druh

O - Ostatní výsledky

CEP obor

JD - Využití počítačů, robotika a její aplikace

OECD FORD obor

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Projekt

<a href="/cs/project/GPP103%2F12%2FP514" target="_blank" >GPP103/12/P514: Detekce anomalií v reálném čase a časově nestálem prostředí</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2013

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů