short-paper

SocialWatch: detection of online service abuse via large-scale social graphs

Authors:

Z. Morley MaoAuthors Info & Claims

ASIA CCS '13: Proceedings of the 8th ACM SIGSAC symposium on Information, computer and communications security

Pages 143 - 148

https://doi.org/10.1145/2484313.2484330

Published: 08 May 2013 Publication History

Abstract

In this paper, we present a framework, SocialWatch, to detect attacker-created accounts and hijacked accounts for online services at a large scale. SocialWatch explores a set of social graph properties that effectively model the overall social activity and connectivity patterns of online users, including degree, PageRank, and social affinity features. These features are hard to mimic and robust to attacker counter strategies. We evaluate SocialWatch using a large, real dataset with more than 682 million users and over 5.75 billion directional relationships. SocialWatch successfully detects 56.85 million attacker-created accounts with a low false detection rate of 0.75% and a low false negative rate of 0.61%. In addition, SocialWatch detects 1.95 million hijacked accounts---among which 1.23 million were not detected previously---with a low false detection rate of 2%. Our work demonstrates the practicality and effectiveness of using large social graphs with billions of edges to detect real attacks.

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Cited By

Diboune ASlimani HNacer HBeghdad Bey K(2024)A comprehensive survey on community detection methods and applications in complex information networksSocial Network Analysis and Mining10.1007/s13278-024-01246-514:1Online publication date: 18-Apr-2024
https://doi.org/10.1007/s13278-024-01246-5
Li ZWang BHuang JJin YXu ZZhang JGao J(2023)A graph-powered large-scale fraud detection systemInternational Journal of Machine Learning and Cybernetics10.1007/s13042-023-01786-w15:1(115-128)Online publication date: 14-Feb-2023
https://doi.org/10.1007/s13042-023-01786-w
Li ZWang HZhang PHui PHuang JLiao JZhang JBu JZhu FChin Ooi BMiao CWang HSkrypnyk IHsu WChawla S(2021)Live-Streaming Fraud Detection: A Heterogeneous Graph Neural Network ApproachProceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining10.1145/3447548.3467065(3670-3678)Online publication date: 14-Aug-2021
https://dl.acm.org/doi/10.1145/3447548.3467065
Show More Cited By

Index Terms

SocialWatch: detection of online service abuse via large-scale social graphs
1. Mathematics of computing
  1. Discrete mathematics
    1. Graph theory
      1. Graph algorithms
2. Security and privacy
  1. Security services
    1. Authentication

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Published In

cover image ACM Conferences

ASIA CCS '13: Proceedings of the 8th ACM SIGSAC symposium on Information, computer and communications security

May 2013

574 pages

ISBN:9781450317672

DOI:10.1145/2484313

General Chairs:
Kefei Chen
Shanghai Jiao Tong University, China
,
Qi Xie
Hangzhou Normal University, China
,
Weidong Qiu
Shanghai Jiao Tong University, China
,
Program Chairs:
Ninghui Li
Purdue University, USA
,
Wen-Guey Tzeng
National Chiao Tung University, Taiwan

Copyright © 2013 ACM.

Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGSAC: ACM Special Interest Group on Security, Audit, and Control

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 May 2013

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ASIA CCS '13

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SIGSAC

ASIA CCS '13: 8th ACM Symposium on Information, Computer and Communications Security

May 8 - 10, 2013

Hangzhou, China

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ASIA CCS '13 Paper Acceptance Rate 35 of 216 submissions, 16%;

Overall Acceptance Rate 418 of 2,322 submissions, 18%

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Cited By

Diboune ASlimani HNacer HBeghdad Bey K(2024)A comprehensive survey on community detection methods and applications in complex information networksSocial Network Analysis and Mining10.1007/s13278-024-01246-514:1Online publication date: 18-Apr-2024
https://doi.org/10.1007/s13278-024-01246-5
Li ZWang BHuang JJin YXu ZZhang JGao J(2023)A graph-powered large-scale fraud detection systemInternational Journal of Machine Learning and Cybernetics10.1007/s13042-023-01786-w15:1(115-128)Online publication date: 14-Feb-2023
https://doi.org/10.1007/s13042-023-01786-w
Li ZWang HZhang PHui PHuang JLiao JZhang JBu JZhu FChin Ooi BMiao CWang HSkrypnyk IHsu WChawla S(2021)Live-Streaming Fraud Detection: A Heterogeneous Graph Neural Network ApproachProceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining10.1145/3447548.3467065(3670-3678)Online publication date: 14-Aug-2021
https://dl.acm.org/doi/10.1145/3447548.3467065
Li ZHui PZhang PHuang JWang BTian LZhang JGao JTang X(2021)What Happens Behind the Scene? Towards Fraud Community Detection in E-Commerce from Online to OfflineCompanion Proceedings of the Web Conference 202110.1145/3442442.3451147(105-113)Online publication date: 19-Apr-2021
https://dl.acm.org/doi/10.1145/3442442.3451147
Shan YZhu JXie YWang JZhou JZhou BXuan Q(2021)Adversarial Attacks on Graphs: How to Hide Your Structural InformationGraph Data Mining10.1007/978-981-16-2609-8_5(93-120)Online publication date: 26-Apr-2021
https://doi.org/10.1007/978-981-16-2609-8_5
Zhu YLuo XLi YBu BZhou KZhang WLu M(2020)Heterogeneous Mini-Graph Neural Network and Its Application to Fraud Invitation Detection2020 IEEE International Conference on Data Mining (ICDM)10.1109/ICDM50108.2020.00098(891-899)Online publication date: Nov-2020
https://doi.org/10.1109/ICDM50108.2020.00098
Fu YXu J(2019)Malware Detection via Extended Label Propagation Through Graph InferenceIEEE Access10.1109/ACCESS.2019.29483747(157830-157840)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2948374
Liu ZChen CYang XZhou JLi XSong LCuzzocrea AAllan JPaton NSrivastava DAgrawal RBroder AZaki MCandan SLabrinidis ASchuster AWang H(2018)Heterogeneous Graph Neural Networks for Malicious Account DetectionProceedings of the 27th ACM International Conference on Information and Knowledge Management10.1145/3269206.3272010(2077-2085)Online publication date: 17-Oct-2018
https://dl.acm.org/doi/10.1145/3269206.3272010
Liu ZChen CZhou JLi XXu FChen TSong LThuraisingham BEvans DMalkin TXu D(2017)POSTERProceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security10.1145/3133956.3138827(2543-2545)Online publication date: 30-Oct-2017
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Fu HXie XRui YGong NSun GChen E(2017)Robust Spammer Detection in MicroblogsACM Transactions on Intelligent Systems and Technology10.1145/30866378:6(1-31)Online publication date: 18-Aug-2017
https://dl.acm.org/doi/10.1145/3086637
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