基于网络流量的用户网络行为被害性分析模型

doi:10.11959/j.issn.1000-0801.2021041

Abstract

Abstract:

The analysis of network victimization is of great significance to the prevention and control of telecom fraud.By studying the network traffic generated by the interaction between users and websites, a victimization identification model of telecom fraud crime based on network behavior flow analysis was proposed, the association rules between different behavior characteristics were analyzed, the behavior sequence features were reconstructed, and the victimization of network behavior sequence with random forest algorithm was evaluated.Based on the network behavior data set of public security organs, the experiment proves that the model can effectively improve the recognition accuracy of network behavior victimization.

Key words: network traffic, network behavior coding, association rules mining, victimization analysis

CLC Number:

TP311

Shengli ZHOU, Xiaoyang XU. Victimization analysis model of user network behavior based on network traffic[J]. Telecommunications Science, 2021, 37(2): 125-134.

Figures/Tables 9

References 26

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行为特征参数	描述
Srcip	源地址
Dstip	目标地址
final_byte_number	总字节
packet_number	总分组数
Dstport	目标端口
Keep_time	持续时长
send_packet	发送分组数
receive_packet	接收分组数
send_len	发送分组长度
receive_len	接收分组长度
sender_header_len	发送tcp头部信息长度
receiver_header_len	接收tcp头部信息长度
Load_size	负载总大小
Send_Load_size	发送负载大小
rec_Load_size	接收负载大小
Feature	行为特征

诈骗类		非诈骗类
种类	数量	种类	数量
博彩	534	网页游戏	453
虚假投资	730	金融投资	323
虚假论坛	364	生活交友	467
虚假借贷	507	彩票	156

关联规则参数	描述
Antecedents	规则前项
Consequents	规则后项
antecedent support	前项支持度
consequent support	后向支持度
Support	前项对后项支持度
Confidence	置信度
Lift	提升度
Leverage	关联性参数1
Conviction	关联性参数2

决策树参数		分类效果变量
Numfeature	Numtree	TP	FP	Precision	Recall	F-Measure	MCC
0	100	0.964	0.095	0.965	0.964	0.964	0.959
2	100	0.963	0.096	0.964	0.963	0.963	0.958
4	100	0.964	0.095	0.965	0.964	0.964	0.959
6	100	0.963	0.096	0.964	0.963	0.963	0.956
8	100	0.963	0.096	0.964	0.963	0.963	0.956
4	20	0.962	0.097	0.963	0.962	0.962	0.955
4	40	0.964	0.095	0.965	0.964	0.964	0.959
4	60	0.965	0.094	0.966	0.965	0.965	0.960
4	80	0.964	0.095	0.965	0.964	0.964	0.959
4	100	0.964	0.095	0.965	0.964	0.964	0.959

Confusion Matrix		VIM-TFCN预测值		C45模型预测值		朴素贝叶斯预测值
Confusion Matrix		TRUE	FALSE	TRUE	FALSE	TRUE	FALSE
真实值	TRUE	2 005	46	1 648	403	1 372	679
	FALSE	32	2 254	443	1 843	359	1 927

Victimization analysis model of user network behavior based on network traffic

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References 26

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