Scalping Anomaly Detection Based on Mobile Internet Traffic Data

A model for detecting unauthorized Apps use events by combined analysis of situation information in an offline service and user behavior in an online environment is proposed. The detection and response to abnormal behavior in the O2O service environment can be focused on providers, whose decisions change dynamically based on the offline market status and conditions. However, the method for identifying the user’s tools and detecting the usage pattern of the service user were developed in the existing online service environment. Thus, in order to identify abnormal behavior in the O2O service environment, we conducted an experiment to identify the abnormal behavior of providers of smart mobility services, a representative O2O service. In the experiment, the range of normal behavior of a taxi drivers was identified, which was prepared on the basis of the test result directly executed by an expert. The optimal features were selected in order to effectively detect abnormal behavior from the event data relating to the service call acceptance behavior. In addition, by processing the collected data based on the selected features by using various machine-learning classification algorithms, we derived a detection and prediction model that is 98.28% accurate with a prediction result of more than 74% based on the F1 score. Based on these results, we expect to be able to respond to abnormal behavior that may occur in various types of O2O services.

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Scalping Anomaly Detection Based on Mobile Internet Traffic Data

Cited by 2 publications

References 29 publications

Malicious SIM Cards Identification Method Based on Telecom Big Data Analytics

Malicious SIM Cards Identification Method Based on Telecom Big Data Analytics

Identification of the Use of Unauthorized Apps in the O2O Service by Combining Online Events and Offline Conditions

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