Anomaly detection method based on cloud edge fusion environment

Abstract

The invention relates to an anomaly detection method based on a cloud edge fusion environment. The anomaly detection method comprises the following steps: step 1, screening m main attributes which have a decisive effect on an event; step 2, dividing a detection area into a plurality of sub-areas, and combining neighbor sub-areas to construct a spatial index tree; and querying the main attribute information of the event by using the spatial index tree, sending the main attribute information to the edge node, and performing abnormality judgment; step 3, performing anomaly detection of a single edge node; step 4, performing anomaly detection on the neighbor edge nodes of the abnormal edge nodes by using a social consciousness relationship; and 5, after the edge layer uploads the main attribute information of the abnormal sub-areas to a cloud layer, enabling the cloud to make a decision. According to the method, whether the neighbor edge nodes of the abnormal edge nodes are abnormal or not is judged through interaction existing in the social consciousness relation, the abnormal nodes can be guided to find the neighbor edge nodes which are possibly abnormal, the anomaly detection range is expanded, and the anomaly detection accuracy is further improved.

Description

technical field [0001] The invention belongs to the technical field of the Internet of Things, and in particular relates to an abnormal detection method based on a cloud-edge fusion environment. Background technique [0002] In order to ensure the safety of people's livelihood and network security, anomaly detection is particularly important in the Internet of Things. Anomaly detection in the Internet of Things environment has become an indispensable task for the development of social security. With the rapid development of Internet of Things technology, smart homes, wearable devices, and external devices for environmental monitoring all provide reliable technical support for anomaly detection. The development of edge computing has brought more convenience to time-critical anomaly detection tasks in the Internet of Things environment. Edge computing mainly processes data through the edge layer close to the data source to reduce the number of devices directly transmitting dat...

AI Technical Summary

Problems solved by technology

[0003] Many domestic and foreign scholars have conducted a lot of research in the field of anomaly detection by using edge computing. Y.Peng et al. [1] A multi-source and multi-dimensional data anomaly detection model is proposed, and a layered edge computing model is used for anomaly detection. First, a single-source data anomaly detection algorithm based on fuzzy theory is designed. Secondly, a multi-source data anomaly detection algorithm implemented at the base station is proposed. This algorithm detects and comprehensively analyzes sensors with position correlation, but all the data at the sensor end needs to be uploaded to the base station. Instead of selectively uploading to the base station, a lot of energy is consumed in this process

[0004] X.Li, Z.Zhou, etc. [2] A multi-modal anomaly detection scheme is proposed, which divides the attributes that determine anomalies into primary attributes and secondary attributes. The primary attribute is a single attribute instead of multiple attributes. lead to false positives and transmission of large amounts of invalid data

[0005] The aforementioned literatures ignore the anomaly detection of edge nodes associated with abnormal edge nodes, resulting in incomplete anomaly detection and inaccurate detection results

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