Abnormal behavior detection method based on large-scale WiFi activity track

Abstract

The invention provides an abnormal behavior detection method based on a large-scale WiFi activity track. The method comprises the following steps: on the basis of a collected MAC record, finding MACs with normal individual behaviors by using a frequent track mining algorithm, extracting the activity feature attributes of these MACs with normal individual behaviors to serve as the input of an SVDD algorithm, establishing a plurality of abnormal behavior detection models to filter a large number of MACs satisfying group behavior rules, thereby not only greatly shortening the time necessary for processing large-scale data, but also ensuring the stability of the abnormal behavior detection method, the feature of serious unbalance of positive and negative samples in the application environment can be well overcome, and accordingly time consistency and space consistency detection is carried out on a single MAC different from the group behavior rules to lock the MAC with abnormal activity more accurately. By adoption of the abnormal behavior detection method provided by the invention, the moving track of a moving object in the public security field can be monitored in real time, abnormal behaviors can be identified accurately in real time, auxiliary judgment is provided for the happening security events, and early warning is provided for the possible security events.

Description

technical field [0001] The invention relates to the technical field of data mining and analysis, in particular to a method for detecting abnormal behaviors based on large-scale WiFi activity tracks. Background technique [0002] In the processing of traditional WiFi scan data, the coordinate information of the mobile terminal is not included in the WiFi scan list, and compared with the GPS track data, the WiFi scan data cannot accurately record the user's actual geographic coordinates and does not have continuous location points, so Traditional WiFi scanning data cannot constitute the elements of time, location, and event on the mobile terminal. [0003] In the prior art, the trajectory data of the mobile terminal is usually recorded by a mobile terminal with a built-in GPS function. However, the GPS can only work when it is turned on, and it consumes a lot of power, and it is in an environment such as a city or indoors where there are occluded objects. The positioning accu...

AI Technical Summary

Problems solved by technology

[0004] However, there is currently no reasonable way to record the travel trajectories of the crowd through WiFi scanning devices, and then use the recorded travel trajectories to detect

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