基于提示学习的电信网络诈骗案件分类方法

doi:10.6040/j.issn.1671-9352.1.2023.040

摘要/Abstract

摘要：

针对电信诈骗案件自动分类技术进行研究，制定基于情境分析的电信网络诈骗分类体系，实现案件文本去标识化隐私保护方法，提出一种基于提示学习的电信网络诈骗案件分类方法，实验结果显示该方法在本文构建的数据集上准确率、F1值等指标均高于基于BERT的分类方法1%~2%。

关键词: 提示学习, 电信网络诈骗, 去标识化, 案件分类

Abstract:

For the automatic classification technology of telecom fraud cases, a classification system of telecom network fraud based on situational analysis is formulated, the privacy protection method of case text de-identification is realized, and accuracy and F1-score of a classification method of telecom network fraud cases based on prompt learning is proposed. The experimental results show that the method is on average 1 to 2 percentage points higher than the BERT-based classification method on the data set constructed in the paper.

Key words: prompt learning, telecom network fraud, de-identificaiton, case classification

中图分类号:

TP391

纪杰,孙承杰,单丽莉,尚伯乐,林磊. 基于提示学习的电信网络诈骗案件分类方法[J]. 《山东大学学报(理学版)》, 2024, 59(7): 113-121.

Jie JI,Chengjie SUN,Lili SHAN,Boyue SHANG,Lei LIN. A prompt learning approach for telecom network fraud case classification[J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2024, 59(7): 113-121.

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参考文献 23

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类别	范围
手机号	11位连续数字
身份证号	18位连续数字(最后一位可能是罗马数字X)
银行卡号	13~19位连续数字
QQ号	5~11位连续数字
微信号	6~20位字母、数字、下划线和减号的组合，以字母开头

去除信息	正则表达式
号码	[\da-zA-Z_]{6, }
出生年月(birth)	(\d{2, 4}年\d{1, 2}月\d{1, 2}日)\|(\d{2, 4}-\d{1, 2}-\d{1, 2})\|(\d{2, 4}.\d{1, 2}.\d{1, 2})\|\d{2, 4}年
出生日期	{birth}出?生)\|(出生(日期\|年月\|于)[: ：]?{birth}
电子邮箱	[a-zA-Z0-9]*@(qq\|163\|gmail)\.com
网址	[hH][tT]{2}[pP][sS]?[: ：; ；]?(//\|//\|//)?[a-zA-Z0-9/.]+[wW]{3}\.)?[a-zA-Z0-9.]+\.(com\|COM\|vip\|VIP\|cc\|CC\|site\|SITE\|top\|TOP)

类别名称	类别定义	覆盖现有类别
购物消费	花费资金，用以购买商品、服务等	冒充电商物流客服类冒充军警购物类网络游戏产品虚假交易类虚假购物、服务类
业务办理	办理各类业务(无需花费资金)	贷款、代办信用卡类虚假征信类
婚恋交友	建立一种关系(婚姻、恋爱、朋友)	网络婚恋、交友类(非虚假网络投资理财类)
配合公务	配合行政、司法等人员执行公务	冒充公检法及政府机关类
人际互助	信任某种人际关系	冒充领导、熟人类
投资盈利	以盈利为目的的行为	刷单返利类虚假网络投资理财类

类别	样本数量	类别	样本数量
刷单返利类	35 459	冒充公检法及政府机关类	4 407
冒充电商物流客服类	13 772	网络游戏产品虚假交易类	2 155
虚假网络投资理财类	11 836	网络婚恋、交友类(非虚假网络投资理财类)	1 654
贷款、代办信用卡类	11 105	冒充军警购物类	1 197
虚假征信类	8 464	网黑案件	1 092
虚假购物、服务类	7 058	总计	102 762
冒充领导、熟人类	4 563

类别名称	类别标签	类别名称	类别标签
刷单返利类	刷单返利	冒充公检法及政府机关类	政府机关
冒充电商物流客服类	电商物流	冒充领导、熟人类	领导熟人
虚假网络投资理财类	投资理财	网络游戏产品虚假交易类	游戏产品
贷款、代办信用卡类	贷款信用	网络婚恋、交友类(非虚假网络投资理财类)	婚恋交友
虚假征信类	虚假征信	冒充军警购物类	军警购物
虚假购物、服务类	购物服务	网黑案件	网黑案件