基于贝叶斯网络的电信网络诈骗受害人特征分析

doi:10.3969/j.issn.1671-1122.2021.12.004

信息网络安全 ›› 2021, Vol. 21 ›› Issue (12): 25-30.doi: 10.3969/j.issn.1671-1122.2021.12.004

基于贝叶斯网络的电信网络诈骗受害人特征分析

罗文华(), 张耀文

中国刑事警察学院,沈阳 110035

收稿日期:2021-08-12 出版日期:2021-12-10 发布日期:2022-01-11
通讯作者: 罗文华 E-mail:luowenhua770404@126.com
作者简介:罗文华（1977—）,男,辽宁,教授,硕士,主要研究方向为信息网络安全与电子数据取证|张耀文（1996—）,男,陕西,硕士研究生,主要研究方向为信息网络安全与网络犯罪侦查
基金资助:
辽宁省科学规划基金重点项目(L21AFX006)

Analysis on Characteristics of Victims of Telecom Network Fraud Based on Bayesian Network

LUO Wenhua(), ZHANG Yaowen

Criminal Investigation Police University of China, Shenyang 110035, China

Received:2021-08-12 Online:2021-12-10 Published:2022-01-11
Contact: LUO Wenhua E-mail:luowenhua770404@126.com

摘要/Abstract

摘要：

电信网络诈骗是典型的非接触式犯罪,防控工作应侧重对受害人特征的分析。现有研究成果多为针对受害人单一特征进行的研究,且依赖案件数量较少,难以全面深入反映特征规律,应用性较弱。文章将电信网络诈骗受害人的自身特征与案件特征作为指标构造贝叶斯网络,建立受害人特征分析模型,进而从案件类型出发,分析易受骗人群;从特征人群出发,分析受骗类型。

关键词: 电信网络诈骗, 贝叶斯网络, 受害人特征, 实证分析

Abstract:

As a typical non-contact crime, the prevention and control of telecom network fraud emphasizes the combing and analysis of the characteristics of victims. However, most of the existing research results are carried out for the single characteristics of victims, and rely on a small number of cases, which is difficult to fully and deeply reflect the characteristics and laws, and the application is weak. This paper took the self characteristics and case characteristics of telecom network fraud victims as indicators, constructs Bayesian network and establishes victim characteristic analysis model. Then, starting from the types of cases, this paper discussed the vulnerable groups. Starting from the characteristic population, this paper expounds its gullible types.

Key words: telecom network fraud, Bayesian network, characteristics of victims, empirical analysis

中图分类号:

TP309

罗文华, 张耀文. 基于贝叶斯网络的电信网络诈骗受害人特征分析[J]. 信息网络安全, 2021, 21(12): 25-30.

LUO Wenhua, ZHANG Yaowen. Analysis on Characteristics of Victims of Telecom Network Fraud Based on Bayesian Network[J]. Netinfo Security, 2021, 21(12): 25-30.

图/表 14

表1

表2

表3

表4

图1

图2

图3

图4

图5

图6

图7

图8

图9

图10

参考文献 10

[1]	MA Zhonghong. Research on Difficulties and Countermeasures of New Network Crime Investigation Represented by Telecommunications Fraud— Based on the Investigation of W Province[J]. Journal of People's Public Security University of China(Social Science Edition), 2018, 34(3):78-86.
	马忠红. 以电信诈骗为代表的新型网络犯罪侦查难点及对策研究——基于W省的调研情况[J]. 中国人民公安大学学报(社会科学版), 2018, 34(3):78-86.
[2]	YIN Ming. Empirical Study on victims of Telecom fraud Cases- Quantitative Statistical Analysis Based on Victim Records[J]. Journal of China Criminal Police College, 2017(3):57-62.
	殷明. 电信诈骗案件受害人的实证研究——基于受害人笔录的量化统计分析[J]. 中国刑警学院学报, 2017(3):57-62.
[3]	ZHANG Zhi, CHEN Feng. Research on Publicity Countermeasures of Telecom Network Fraud Prevention-An Empirical Analysis Based on the Characteristics of Victims[J]. Journal of Guangxi Police College, 2021, 34(2):41-49.
	张芷, 陈峰. 电信网络诈骗防范宣传对策研究——基于被害人特征的实证分析[J]. 广西警察学院学报, 2021, 34(2):41-49.
[4]	YAN Zhiyong, XU Congfu, PAN Yunhe. Improving Naive Bayes Classifier by Dividing Its Decision Regions[J]. Journal of Zhejiang University SCIENCE C, 2011, 12(8):647-657. doi: 10.1631/jzus.C1000437 URL
[5]	QIN Xiaohu, LIU Li, ZHANG Ying. A Traffic Accident Prediction Method Based on Bayesian Network Model[J]. Computer Simulation, 2005, 22(11):230-232.
	秦小虎, 刘利, 张颖. 一种基于贝叶斯网络模型的交通事故预测方法[J]. 计算机仿真, 2005, 22(11):230-232.
[6]	LIN Shimin, TIAN Fengzhan, LU Yuchang. Construction of Bayesian Network and Its Application in Data Mining[J]. Journal of Tsinghua University(Natural Science Edition), 2001, 41(1):49-52.
	林士敏, 田凤占, 陆玉昌. 贝叶斯网络的建造及其在数据采掘中的应用[J]. 清华大学学报(自然科学版), 2001, 41(1):49-52.
[7]	LUO Wenhua, MA Xiaohan. Empirical Study on Multi-level Network Public Opinion Early Warning Based on Bayesian Network[J]. Information Science, 2021, 39(7):68-74.
	罗文华, 马晓晗. 基于贝叶斯网络的多级次网络舆情预警实证研究[J]. 情报科学, 2021, 39(7):68-74.
[8]	ZHANG Yiwen, QI Jiayin, FANG Binxing, et al. Research on Network Public Opinion Early Warning of Unconventional Crisis Events Based on Bayesian Network Modeling[J]. Library and Information Work, 2012, 56(2):76-81.
	张一文, 齐佳音, 方滨兴, 等. 基于贝叶斯网络建模的非常规危机事件网络舆情预警研究[J]. 图书情报工作, 2012, 56(2):76-81.
[9]	TIAN Junjing, LAN Yuexin, XIA Yixue, et al. Identification and Empirical Study of Online Public Opinion Reversal Based on Decision Tree Method[J]. Journal of Information, 2019, 38(8):121-125, 171.
	田俊静, 兰月新, 夏一雪, 等. 基于决策树方法的网络舆情反转识别与实证研究[J]. 情报杂志, 2019, 38(8):121-125,171.
[10]	WEI Keqiang, SONG Meiqiong. Investigation Difficulties and Solutions of Network Telecom Fraud Cases[J]. Crime Research, 2020(1):56-60.

类型	节点名称（Type）	值
假冒身份类	FakeID	1
网络交友类	OnlineFriend	2
网络贷款类	OnlineLoan	3
虚假交易投资类	FakeTransInvest	4

性别	节点名称（Gender）	值
男	Male	1
女	Female	2

年龄	节点名称（Age）	区间	值
青少年	Teenager	0~28	1
青壮年	YoungAdult	29~44	2
中老年	MiddleOld	45~99	3

受骗金额	节点名称（Damage）	区间	值
低	Low	小于18000	1
中	Middle	18000~60000	2
高	High	大于60000	3

基于贝叶斯网络的电信网络诈骗受害人特征分析

Analysis on Characteristics of Victims of Telecom Network Fraud Based on Bayesian Network

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

图/表 14

参考文献 10

相关文章 5

编辑推荐

Metrics

本文评价

[1]	丁家伟, 刘晓栋. 基于ELECTRA-CRF的电信网络诈骗案件文本命名实体识别模型[J]. 信息网络安全, 2021, 21(6): 63-69.
[2]	朱容辰, 李欣, 林小暖. 基于贝叶斯网络的视频专网安全风险分析方法[J]. 信息网络安全, 2021, 21(12): 91-101.
[3]	肖彪, 闫宏强, 罗海宁, 李炬成. 基于差分隐私的贝叶斯网络隐私保护算法的改进研究[J]. 信息网络安全, 2020, 20(11): 75-86.
[4]	郝文江, 徐丽萍, 姜劲蕾, 李翠翠. 电信网络诈骗犯罪治理技术研究[J]. 信息网络安全, 2016, 16(9): 213-217.
[5]	何一青. 一种基于有向二分图模型和贝叶斯网络的入侵检测方法[J]. , 2012, 12(8): 0-0.