Your Style Your Identity: Leveraging Writing and Photography Styles for Drug Trafficker Identification in Darknet Markets over Attributed Heterogeneous Information Network

Zhang, Yiming; Fan, Yujie; Song, Wei; Hou, Shifu; Ye, Yanfang; Li, Xin; Zhao, Liang; Shi, Chuan; Wang, Jiabin; Xiong, Qi

doi:10.1145/3308558.3313537

Cited by 39 publications

(40 citation statements)

References 25 publications

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“…And then align users based on those embedding vectors. uStyle-uID [45] utilizes user writing and photo style to predict the label of identity pairs in Darknet network, while iDev [4] predict the label of identity pairs based on the feature coding published by user in public social coding platforms. DALAUP [3] utilizes active learning to solve the problem that labeled data is difficult to obtain in the UIL task.…”

Section: User Identities Linkagementioning

confidence: 99%

RLINK: Deep reinforcement learning for user identity linkage

Cao

et al. 2020

World Wide Web

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User identity linkage is a task of recognizing the identities of the same user across different social networks (SN). Previous works tackle this problem via estimating the pairwise similarity between identities from different SN, predicting the label of identity pairs or selecting the most relevant identity pair based on the similarity scores. However, most of these methods fail to utilize the results of previously matched identities, which could contribute to the subsequent linkages in following matching steps. To address this problem, we transform user identity linkage into a sequence decision problem and propose a reinforcement learning model to optimize the linkage strategy from the global perspective. Our method makes full use of both the social network structure and the history matched identities, meanwhile explores the long-term influence of processing matching on subsequent decisions. We conduct extensive experiments on real-world datasets, the results show that our method outperforms the state-of-the-art methods.

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Section: User Identities Linkagementioning

confidence: 99%

RLINK: Deep reinforcement learning for user identity linkage

Cao

et al. 2020

World Wide Web

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“…For traditional machine learning-based methods, using the augment features (i.e.,f-7) as the inputs, we consider typical classification models of Decision Tree (DT) and Support Vector Machine (SVM) and compare them with the DNN classifier described in Section 2.4. We also compare our system dStyle-GAN with the existing system uStyle-uID [51]. In uStyle-uID, we redefine the entities and relations for AHIN and rebuilt meta-paths in uStyle-uID to learn node embeddings for drug identification.…”

Section: Comparisons With Competing Approachesmentioning

confidence: 99%

“…To combat illicit drug trafficking in darknet makrets, there have been many research efforts on darknet market data analysis: for examples, [6,11,15,28,42,47,51] focus on drug trafficker identification and trafficking network investigation; while [7-10, 12, 14, 16, 44] explore statistical methodologies to analyze illicit drugs traded in the markets. Since the identification and analysis of illicit drugs can not only provide valuable insights to profile drug traffickers but also enable the deep understanding of dynamics and evolution of drug trafficking activities, in this work, we focus on the illicit drug identification and investigation.…”

Section: Related Workmentioning

confidence: 99%

“…To combat the evolving cybercrimes of online drug trafficking, there's an imminent need to gain deep insights into the dynamics and operations of illicit activities in underground markets and thus enable law enforcement for proactive interventions. To address this challenge, there have been many research efforts on the investigation of illicit drug trafficking in darknet markets, which mainly can be categorized into two fields: i) drug trafficker identification and trafficking network investigation [11,28,47,51]; ii) understanding dynamics and evolution of illicit drugs [8,14,16,44]. In this work, we focus on the second one, since the identification and analysis of illicit drugs can not only provide valuable insights to profile drug traffickers but also facilitate market scale estimation and trend prediction.…”

Section: Introductionmentioning

confidence: 99%

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dStyle-GAN: Generative Adversarial Network based on Writing and Photography Styles for Drug Identification in Darknet Markets

Zhang

Qian

Fan

et al. 2020

Annual Computer Security Applications Conference

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“…HIN has been intensively deployed to various applications, such as authorship identification [40], malware detection [18], [12], [38], and health intelligence [14], [13]. To reduce the high computation and space cost in network mining, many efficient network embedding methods have been proposed, including homogeneous network representation learning (e.g., DeepWalk [26], node2vec [17], LINE [32], and TADW [36]) and HIN representation learning (e.g., ESim [28], metap-ath2vec [11] and HIN2vec [37]).…”

Section: Related Workmentioning

confidence: 99%

iTrustSO

Chen

Hou

et al. 2019

Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

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Despite the apparent benefits of modern social coding paradigm such as Stack Overflow, its potential security risks have been largely overlooked (e.g., insecure codes could be easily embedded and distributed). To address this imminent issue, in this paper, we bring a significant insight to leverage both social coding properties and code content for automatic detection of insecure code snippets in Stack Overflow. To determine if the given code snippets are insecure, we not only analyze the code content, but also utilize various kinds of relations among users, badges, questions, answers and code snippets in Stack Overflow. To model the rich semantic relationships, we first introduce a structured heterogeneous information network (HIN) for representation and then use meta-path based approach to incorporate higher-level semantics to build up relatedness over code snippets. Later, we propose a novel hierarchical attention-based sequence learning model named CodeHin2Vec to seamlessly integrate node (i.e., code snippet) content with HIN-based relations for representation learning. After that, a classifier is built for insecure code snippet detection. Integrating our proposed method, an intelligent system named iTrustSO is accordingly developed to address the code security issues in modern software coding platforms. Comprehensive experiments on the data collections from Stack Overflow are conducted to validate the effectiveness of our developed system iTrustSO by comparisons with alternative methods.

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Your Style Your Identity: Leveraging Writing and Photography Styles for Drug Trafficker Identification in Darknet Markets over Attributed Heterogeneous Information Network

Cited by 39 publications

References 25 publications

RLINK: Deep reinforcement learning for user identity linkage

RLINK: Deep reinforcement learning for user identity linkage

dStyle-GAN: Generative Adversarial Network based on Writing and Photography Styles for Drug Identification in Darknet Markets

iTrustSO

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