Fraud phone recognition method, system and equipment based on multi-source features

Abstract

The invention discloses a fraud phone recognition method, system and equipment based on multi-source characteristics, and the method comprises the steps: a user selects a normal number, a promotion number and a fraud number, constructs a user classification which is more practical, and based on the multi-source characteristic indexes of the selected user, the basic characteristics of user call data, and the basic call characteristics of the user, image characteristic, user position and Internet-surfing characteristic are included; structural features of a user two-degree network is extracted based on a Struct2Vec image network model with a similar image structure; multi-point and one-line fraud mode structures and the like are identified; based on the fact that user two-degree call data is converted into call time sequence data, a feature combination based on a time sequence is extracted, and on the basis of constructing the multi-source feature, a sample data set is balanced by using an oversampling method Borderline-SMOTE, a classification model for normal, fraud and promotion identification is finally constructed. The model carries out training prediction by using a plurality of different integrated learning combination modes, and accurate and effective identification of fraud calls is realized in combination with a black and white list filtering mechanism.

Description

technical field [0001] The invention belongs to the field of information technology, and in particular relates to a fraud phone identification method, system and equipment based on multi-source features. Background technique [0002] With the continuous development of the communication industry, more and more users enjoy the convenience that communication brings to life, but at the same time, more and more fraudulent calls are emerging, and a large number of groups or individuals use Attacks and other means harass the target group and defraud people of money. Similar scam calls emerge in endlessly in life, which seriously affects the user experience and brings great inconvenience to the daily life of users. [0003] At present, methods for identifying fraudulent calls are based on some basic feature extraction using machine learning or deep learning methods for identification, such as: [A fraudulent application detection method based on deep learning], [An analysis of fraudu...

AI Technical Summary

Problems solved by technology

[0002] With the continuous development of the communication industry, more and more users enjoy the convenience that communication brings to life, but at the same time, more and more fraudulent calls are emerging, and a large number of groups or individuals use Attacks and other methods harass the target group and defraud people of money. Similar scam calls emerge in endlessly in life, which seriously affects the user experience and brings great inconvenience to the daily life of users.

[0003] At present, methods for identifying fraudulent calls are based on some basic feature extraction using machine learning or deep learning methods for identification, such as: [A fraudulent application detection method based on deep learning], [An analysis of fraudulent calls based on multidimensional time series] Methods], [method and system for identifying fraudulent phone numbers], etc., there are also methods for identifying fraudulent patterns, such as: [a method for detecting fraudulent calls based on intent understanding technology], [a method for identifying fraudulent calls based on graph embedding] method], etc., but based on the above patents, we found some points that the existing patents did not pay attention to:

[0004] In real life, there are a variety of other numbers that are similar to fraudulent calls and have the characteristics of high frequency and high frequency, such as: sales calls, express delivery calls, taxi Didi calls, company calls, etc. Especially sales calls, which have a short life cycle and are difficult to identify in a timely and effective manner through the black and white lists, but the existing technology does not mention the various interference situations in this actual environment

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