Application of machine learning in the fake user identification of IoT

Rongfang ZHANG; Dandan XU; Yuanguang WANG; Siyu PAN; Zhengmao LI

doi:10.11959/j.issn.1000-0801.2019090

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Application of machine learning in the fake user identification of IoT

Operation technology wide Angle | 更新时间：2024-06-05

- Application of machine learning in the fake user identification of IoT
- Telecommunications Science Vol. 35, Issue 7, Pages: 136-144(2019)
- 作者机构：
  
  1. 中国联合网络通信有限公司研究院，北京 100176
  2. 中国联合网络通信集团有限公司电子商务中心，北京 100033
  3. 中国联合网络通信有限公司重庆市分公司，重庆 401121
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.2019090
  CLC： TP18；TN929.5
- Published Online：2019-07，
  
  Published：20 July 2019
- 稿件说明：
移动端阅览
Rongfang ZHANG, Dandan XU, Yuanguang WANG, et al. Application of machine learning in the fake user identification of IoT[J]. Telecommunications science, 2019, 35(7): 136-144.
DOI：

Rongfang ZHANG, Dandan XU, Yuanguang WANG, et al. Application of machine learning in the fake user identification of IoT[J]. Telecommunications science, 2019, 35(7): 136-144. DOI： 10.11959/j.issn.1000-0801.2019090.

摘要

随着通信技术的发展，物联网卡和5G技术将得到大规模应用，但存在个别企业利用物联网卡资费便宜、没有实名制等特点从中非法牟利、破坏社会稳定的问题，不利于行业健康发展。因此如何识别虚假用户成为物联网行业研究的重要课题。主要研究了在实时海量的物联网终端数据中，如何运用机器学习模型高效地识别疑似虚假用户。具体来看，通过研究相关数据的特征，采用基于正样本和未标记样本的半监督式学习模型建立实时监控异常行为的模型，达到识别物联网行业中潜在虚假用户的目的。本研究成果体现在节约大量人力物力的同时，可以帮助相关部门、人员及时发现用户的异常行为，采取相应的措施避免产生较大损失，具有广泛的行业应用前景。

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.

关键词

Keywords

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