Anomaly detection method based on data incremental graphs

Abstract

The invention discloses an anomaly detection method based on data incremental graphs. The anomaly detection method includes the following steps that detection data in a current monitoring zone of a wireless sensor network are collected and preprocessed, and an event zone is determined; data sets relevant to a current event are acquired, a graph model is utilized to abstractly generalize event data, and the event data are converted into the event data incremental graphs; a graph similarity algorithm based on structure correlation is utilized to search an event mode graph database for event mode graphs similar to the event graphs and judge the type of the current event, wherein the event mode graph database is a set of the event mode graphs; the event mode graphs are the event data incremental graphs and abstract description for types of events; by the adoption of the graph similarity query algorithm based on the structure correlation, the graph similarity query problem is converted into the sequence similarity query problem, and therefore query complexity is effectively reduced. By the adoption of the anomaly detection method based on the data incremental graphs, the event graphs can be acquired based on domain expert knowledge or data analysis and used for detecting complex events, the detection efficiency of the events is improved, and the false alarm rate is reduced.

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 increment graph. 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 a large amount of noise data, and abnormal events in the sensor network, etc. 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, some users require not only to detect which sensor node data is anomalous, but also to detect the specific anomalous event types 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 applica...

AI Technical Summary

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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