Anomaly detection method based on data snapshot graphs

An anomaly detection and data technology, applied in electrical digital data processing, special data processing applications, instruments, etc., can solve the problem of high computational complexity

Inactive Publication Date: 2014-02-05
SOUTHEAST UNIV
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Problems solved by technology

The method based on the largest common subgraph is a direct calculation of similarity, which uses the calculation of subgraph isomorphism, so the computational complexity is relatively high

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  • Anomaly detection method based on data snapshot graphs
  • Anomaly detection method based on data snapshot graphs
  • Anomaly detection method based on data snapshot graphs

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Embodiment Construction

[0081] The present invention will be further described below in conjunction with the accompanying drawings.

[0082] Scheme principle description

[0083] In a wireless sensor network, the occurrence of an event must be reflected in the state change of sensor node monitoring data, and the inherent characteristics of the event will derive the specific data mode of the event. If abstract feature extraction is performed on the data to find out this data pattern, when the sensor network presents this data pattern again, the occurrence of corresponding events can be determined according to the similarity of the data pattern. The wireless sensor network is data-centric, and there is a strong temporal-spatial correlation between the data. If the data of a certain node at a certain moment is regarded as the vertex in the data graph, the temporal-spatial correlation between the data is regarded as the The edges in the graph can naturally use the graph model to describe the event chara...

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Abstract

The invention discloses an anomaly detection method based on data snapshot graphs. The method includes the first step of carrying out acquisition and pretreatment on detection data in a current monitored area of a wireless sensor network to determine an event area, the second step of obtaining a dataset related to a current event, using a graph model to abstractly summarize event data and converting the event data into the event data snapshot graphs, and the third step of carrying out query in an event mode pattern database through a graph similarity algorithm based on structural correlativity, searching for event mode patterns similar to the event graphs and judging the type of the current event, wherein the event mode pattern database is a collection of the event mode patterns, and the event mode patterns are the event data snapshot graphs which represent for abstract description of the type of the event. According to the anomaly detection method based on the data snapshot graphs, the event graphs can be obtained on the basis of domain expert knowledge or on the basis of data analysis. The method has the advantages of being used for detection of the complex event, improving event detection efficiency and reducing the false alarm rate.

Description

technical field [0001] The invention relates to an abnormal detection method of a wireless sensor network, in particular to an abnormal detection method based on a data snapshot. Background technique [0002] State of the Art of Anomaly Detection in Wireless Sensor Networks [0003] In wireless sensor networks, there are various reasons for sensor node data anomalies, such as sensor node failures, collected data containing noise, and abnormal events in sensor networks. Anomaly detection of wireless sensor network is to detect these abnormal data and feed back to users so that users can make corresponding decisions. However, many users not only require to detect which sensor node data has anomalies, but also require to detect the specific types of anomalies that cause these data anomalies. Such anomaly detection is also called abnormal event detection or event detection, which has important practical significance. For example, in the application of fire detection, when the...

Claims

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Application Information

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IPC IPC(8): H04W24/04H04W84/18G06F17/30
Inventor 吕建华张柏礼魏巨巍
Owner SOUTHEAST UNIV
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