Abstract:

With the development of communication technology, IoT cards and 5G technologies will be applied on a large scale. However, there are some companies have taken advantage of the fact that the price of SIM cards of IoT is cheap and the cards do not have real-name registration system. It is harmful to social stability, which is not conducive to the development of IoT industry. So how to identify these fake users has become an important topic in IoT industry. The purpose was to use machine learning models to identify users who have high suspiciousness effectively. By studying the characteristics of relevant data, a semi-supervised learning model based on positive and unlabeled samples was used to establish a real-time abnormal behavior monitoring model to identify potential fake users in the IoT industry users. At the same time, the model greatly enhanced the working efficiency and has saved the manpower physical resources. Also, it can help relevant departments and governments to discover the abnormal behavior of users in time and take corresponding measures to avoid large losses. So, the proposed method really has broad application prospects in the industry.

Key words: IoT, positive and unlabeled semi-unsupervised model, Na?ve Bayesian classifier, random forest, support vector machine, SPY classifier