poster

POSTER: Neural Network-based Graph Embedding for Malicious Accounts Detection

Authors:

Feng Xu,

Le SongAuthors Info & Claims

CCS '17: Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security

Pages 2543 - 2545

https://doi.org/10.1145/3133956.3138827

Published: 30 October 2017 Publication History

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Abstract

We present a neural network based graph embedding method for detecting malicious accounts at Alipay, one of the world's leading mobile payment platform. Our method adaptively learns discriminative embeddings from an account-device graph based on two fundamental weaknesses of attackers, i.e. device aggregation and activity aggregation. Experiments show that our method achieves outstanding precision-recall curve compared with existing methods.

References

[1]

Qiang Cao, Xiaowei Yang, Jieqi Yu, and Christopher Palow. 2014. Uncovering large groups of active malicious accounts in online social networks Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security. ACM, 477--488.

Google Scholar

[2]

Hanjun Dai, Bo Dai, and Le Song 2016. Discriminative embeddings of latent variable models for structured data International Conference on Machine Learning. 2702--2711.

Google Scholar

[3]

Junxian Huang, Yinglian Xie, Fang Yu, Qifa Ke, Martin Abadi, Eliot Gillum, and Z Morley Mao 2013. Socialwatch: detection of online service abuse via large-scale social graphs Proceedings of the 8th ACM SIGSAC symposium on Information, computer and communications security. ACM, 143--148.

Google Scholar

[4]

Gianluca Stringhini, Pierre Mourlanne, Gregoire Jacob, Manuel Egele, Christopher Kruegel, and Giovanni Vigna 2015. Evilcohort: detecting communities of malicious accounts on online services. USENIX.

Google Scholar

[5]

Yinglian Xie, Fang Yu, Kannan Achan, Rina Panigrahy, Geoff Hulten, and Ivan Osipkov. 2008. Spamming botnets: signatures and characteristics. ACM SIGCOMM Computer Communication Review Vol. 38, 4 (2008), 171--182.

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Google Scholar

[6]

Yao Zhao, Yinglian Xie, Fang Yu, Qifa Ke, Yuan Yu, Yan Chen, and Eliot Gillum. 2009. BotGraph: Large Scale Spamming Botnet Detection. NSDI, Vol. Vol. 9. 321--334. endthebibliography

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Cited By

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Huang HZhang XWang JGao CLi XZhu RMa Q(2024)PEAE-GNN: Phishing Detection on Ethereum via Augmentation Ego-Graph Based on Graph Neural NetworkIEEE Transactions on Computational Social Systems10.1109/TCSS.2023.334907111:3(4326-4339)Online publication date: Jun-2024
https://doi.org/10.1109/TCSS.2023.3349071
Wu ECui HChen ZWelsch R(2022)TreeagoNeurocomputing10.1016/j.neucom.2022.03.021489:C(429-440)Online publication date: 7-Jun-2022
https://dl.acm.org/doi/10.1016/j.neucom.2022.03.021
Lavanya PShankar Sriram V(2022)Detection of Insider Threats Using Deep Learning: A ReviewComputational Intelligence in Data Mining10.1007/978-981-16-9447-9_4(41-57)Online publication date: 7-May-2022
https://doi.org/10.1007/978-981-16-9447-9_4

Index Terms

POSTER: Neural Network-based Graph Embedding for Malicious Accounts Detection
1. Security and privacy
  1. Cryptography

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Published In

CCS '17: Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security

October 2017

2682 pages

ISBN:9781450349468

DOI:10.1145/3133956

General Chair:
Bhavani Thuraisingham
The University of Texas at Dallas, USA
,
Program Chairs:
David Evans
University of Virginia
,
Tal Malkin
Columbia University
,
Dongyan Xu
Purdue University

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

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Published: 30 October 2017

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CCS '17: 2017 ACM SIGSAC Conference on Computer and Communications Security

October 30 - November 3, 2017

Texas, Dallas, USA

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CCS '17 Paper Acceptance Rate 151 of 836 submissions, 18%;

Overall Acceptance Rate 1,261 of 6,999 submissions, 18%

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CCS '25

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ACM SIGSAC Conference on Computer and Communications Security

October 13 - 17, 2025

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Huang HZhang XWang JGao CLi XZhu RMa Q(2024)PEAE-GNN: Phishing Detection on Ethereum via Augmentation Ego-Graph Based on Graph Neural NetworkIEEE Transactions on Computational Social Systems10.1109/TCSS.2023.334907111:3(4326-4339)Online publication date: Jun-2024
https://doi.org/10.1109/TCSS.2023.3349071
Wu ECui HChen ZWelsch R(2022)TreeagoNeurocomputing10.1016/j.neucom.2022.03.021489:C(429-440)Online publication date: 7-Jun-2022
https://dl.acm.org/doi/10.1016/j.neucom.2022.03.021
Lavanya PShankar Sriram V(2022)Detection of Insider Threats Using Deep Learning: A ReviewComputational Intelligence in Data Mining10.1007/978-981-16-9447-9_4(41-57)Online publication date: 7-May-2022
https://doi.org/10.1007/978-981-16-9447-9_4

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