Research on extracting risk control rules for Internet of Things business
Ziyan Lin, Chunlai Zhou · 2018
The core of the risk management system of mobile industry is the risk detecting rules that usually are divided into two categories: normal detecting rules and abnormal detecting rules, the former find the suspected fraud customers using of the features of normal customer behaviors, and the latter find suspected fraud directly using of the features of abnormal customer behaviors. Both features of normal and abnormal customer behaviors are from the expert knowledge or data analysis. In recent years, the services of Internet of things have quickly risen in the mobile industry, which supply the infrastructure network to customers, and the consequent the frauds emerge. To ensure the business healthily development, the risk controlling and management system is required. In the paper, the big data analysis for mobile industry is introduced, and the basic approaches to extract expert knowledge from data set are explained, and the association analysis is presented for mobile data to obtain the risk controlling rules, at last the experiment and result analysis are given that proved the approach in the paper is reasonable and effective.