Telephone number identification method and device, computer equipment and computer storage medium

Abstract

The invention discloses a telephone number identification method and device, computer equipment and a computer storage medium. The telephone number identification method comprises the following steps:acquiring telephone identifiers of all first-class calling numbers; extracting first-type calling numbers of which all telephone identifiers are junk telephones and forming a number set; extracting frequency spectrums from the fourth digit to the seventh digit of all telephone numbers in the number set; and judging the second type of calling numbers as junk calls according to the frequency spectrum. According to the embodiment of the invention, the number characteristics of the junk phone can be effectively and efficiently identified by constructing the frequency spectrum of the number of thejunk phone, so that the junk phone judgment speed and accuracy are improved.

Description

technical field [0001] The invention belongs to the technical field of data analysis, and in particular relates to a telephone number identification method, device, computer equipment and computer storage medium. Background technique [0002] With the popularity of smart phones, spam calls and harassing calls are showing a blowout trend, and the growth rate is obvious. They are becoming a threat to user information security in the mobile Internet era after computer viruses and phishing websites, bringing huge troubles to public life, and threatening public privacy and economic caused huge losses. Recently, many tragedies of communication and information fraud have been continuously exposed. The hazards of spam calls, harassing calls, and fraudulent calls have expanded from property safety to personal safety. Currently, an average of 8.6 billion spam calls are generated each month. The Ministry of Industry and Information Technology rectified the number changing software f...

AI Technical Summary

Problems solved by technology

However, after determining the characteristics, simply clustering them can no longer adapt to the ever-changing spam / scam scene

According to crowdsourced phone calls, there are as many as six types of spam / scam calls, and the attributes of spam / scam calls are not a simple convex set, and the results of the clustering algorithm are not steady state, resulting in certain inconsistencies in the identification results of spam / scam calls. Deterministic, and for the self-learning solution, there is also a lack of specific algorithms and processes

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