Negative feature selection algorithm for anomaly detection in real time

• Autorzy: Hryniów K. , Dzieliński A.
• Języki publikacji: EN

Abstrakty

EN

Anomaly detection methods are of common use in many fields, including databases and large computer systems. This article presents new algorithm based on negative feature selection, which can be used to find anomalies in real time. Proposed algorithm, called Negative Feature Selection algorithm (NegFS) can be also used as first step for preprocessing data analyzed by neural networks, rule-based systems or other anomaly detection tools, to speed up the process for large and very large datasets of different types.

Słowa kluczowe

EN

anomaly detection; feature selection; frequent pattern mining; neural networks; rule-based systems

• Wydawca: Wydawnictwo Uniwersytetu Przyrodniczo-Humanistycznego w Siedlcach
• Czasopismo: Studia Informatica : systems and information technology
• Rocznik: 2011
• Tom: Vol. 1-2(15)
• Strony: 15--23
• Opis fizyczny: Bibliogr. 10 poz., tab., wykr.

Twórcy

autor

Hryniów K.

• Warsaw University of Technology Institute of Control and Industrial Electronics Koszykowa St. 75, 00-662, Warsaw, Poland

autor

Dzieliński A.

• Warsaw University of Technology Institute of Control and Industrial Electronics Koszykowa St. 75, 00-662, Warsaw, Poland

Bibliografia

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