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Design of anomaly detection testing platform

Lu Chao

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Abstract

Testing platform of anomaly detection with different algorithms is proposed.For the adaption to the mass of distributed net-works,the flatform divides the networks into different network segments,where is set up by a detection IC,which adds up net data given by diverse ICs to anomaly detection parts.Then according to the anomaly analyzed data even the result,the platform whose anomaly detection algorithms can be replaced would give an immediate alarm to the possible invading actions.Eventually,an experiment based on statistical anomaly detection algorithms is introduced,then a result of anomaly detection is offered.

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What this paper is about

Testing platform of anomaly detection with different algorithms is proposed.For the adaption to the mass of distributed net-works,the flatform divides the networks into different network segments,where is set up by a detection IC,which adds up net data given by diverse ICs to anomaly detection parts.Then according to the anomaly analyzed data even the result,the platform whose anomaly detection algorithms can be replaced would give an immediate alarm to the possible invading actions.Eventually,an experiment based on statistical anomaly detection algorithms is introduced,then a result of anomaly detection is offered.

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Available abstract

Testing platform of anomaly detection with different algorithms is proposed.For the adaption to the mass of distributed net-works,the flatform divides the networks into different network segments,where is set up by a detection IC,which adds up net data given by diverse ICs to anomaly detection parts.Then according to the anomaly analyzed data even the result,the platform whose anomaly detection algorithms can be replaced would give an immediate alarm to the possible invading actions.Eventually,an experiment based on statistical anomaly detection algorithms is introduced,then a result of anomaly detection is offered.

Key concepts: Anomaly detection, Computer science, Anomaly (physics), Set (abstract data type), Data mining, Data set, Constant false alarm rate, False alarm

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