一种利用注意力增强卷积的暗网用户对齐方法

doi:10.19665/j.issn1001-2400.2023.04.020

Abstract

Abstract:

Dark network users engage in a large number of illegal and criminal activities in the underground market.The anonymity of the dark network brings great convenience to the communication between users of the dark network,but great difficulties to the police.In recent years,the deep neural network has been widely successful in various fields,and more and more researchers have begun to use the neural network to identify anonymous network text authors.In order to better align users in the dark web and find more different users with the same identity,we use the neural network method to identify and align users in the dark web.However,the existing methods focus mainly on the short text and are not good at dealing with the global and long sequence information.In this paper,we propose a self-attention mechanism to enhance the convolution operator and use long sequence information to strengthen the user representation,named DACN.DACN starts from the text content,and multiple account associations are carried out for anonymous dark web users to aggregate information from multiple anonymous accounts,proving mores clues for obtaining the users’true identity.Our recent analysis involves conducting a thorough assessment of two distinct dark web market forums,whereby we evaluate our methodology in comparison to the current state-of-the-art techniques.Experimental results show that our approach is remarkably effective,with a demonstrated average mean retrieval ranking (MRR) enhancement of 2.9% and 3.6%,as well as an improved Recall@10 of 2.3% and 3.0%.This evaluation offers robust evidence of the efficacy of our approach in dark web market forums.

Key words: text embedding, attention mechanism, convolutional networks, long sequence information

CLC Number:

TP18

YANG Yanyan,DU Yanhui,LIU Hongmeng,ZHAO Jiapeng,SHI Jinqiao,WANG Xuebin. Dark web author alignment based on attention augmented convolutional networks[J].Journal of Xidian University, 2023, 50(4): 206-214.

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References 27

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数据集名称	训练集帖子数量	测试集帖子数量	训练集用户数量	测试集用户数量
丝绸之路	379 382	381 959	6 585	8 865
广场市场	175 978	179 482	3 115	4 209

方法	广场市场		丝绸之路
方法	MRR	Recall@10	MRR	Recall@10
只考虑文本建模
TextCNN(2014)^[1]	0.126	0.214	0.036	0.073
Transformer (2017)^[11]	0.091	0.133	0.026	0.043
DACN	0.131	0.179	0.080	0.124
考虑用户建模
IUR (2019)^[2]	0.114	0.218	0.109	0.190
SYSML-CNN (2021)^[3]	0.152	0.279	0.157	0.252
SYSML-Transformer (2021)^[3]	0.133	0.235	0.103	0.175
DACN	0.181	0.302	0.193	0.282

Dark web author alignment based on attention augmented convolutional networks

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