Aiding the Detection of Fake Accounts in Large Scale Social Online Services

By Qiang Cao, Michael Sirivianos, Xiaowei Yang, Tiago Pregueiro. 2012

559 citations recorded by the source corpus.

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Citations in the corpus

Attacking Graph-based Classification via Manipulating the Graph Structure (2019, methodology): “Moreover, collective classiﬁcation methods were deployed in industry for malware detection [14,57] and Sybil detection in social networks [9,10].”
CrowdTarget: Target-based Detection of Crowdturfing in Online Social Networks (2015, background): “Second, graph-based methods [9, 10, 13, 30, 36, 37] detect malicious accounts by using the observation that malicious accounts usually have few connections with normal accounts.”
Online Human-Bot Interactions: Detection, Estimation, and Characterization (2017, background): “SybilRank is a system developed to identify attacks from their underlying topology (Cao et al. 2012).”
Using naive bayes to detect spammy names in social networks (2013, background): “Many previous authors have considered the problem of detecting spam and fake accounts in social networks, using signals such as clickstream patterns [30, 29], message-sending activity and content [3, 14], and properties of the social graph [8, 7, 6].”
FRAUDAR: Bounding Graph Fraud in the Face of Camouflage (2016, methodology): “Several methods have used HITS [15]-like ideas to detect fraud in graphs [3,6,9,11,30].”
Deep Neural Networks for Bot Detection (2018, background): “Some of them published studies showing the effectiveness of some implementations, e.g., SybilRank [8], the Facebook Immune System [52], and others [3].”
Graph-based Security and Privacy Analytics via Collective Classification with Joint Weight Learning and Propagation (2018, methodology): “State-of-the-art collective classiﬁcation methods [2], [5], [6], [11], [13]–[15], [17]–[20], [22]–[29] have three key steps.”
The Paradigm-Shift of Social Spambots: Evidence, Theories, and Tools for the Arms Race (2017, unknown): “010-13 Viswanath, Mislove, Gummadi et al. [40] 2015 Viswanath, Mislove, Gummadi et al. [39] 2011 Cresci et al. [13] 2016 Stein et al. [32] 2011 Thomas et al. [36] 2011 Gao et al. [16] 2012 Cao et al. [8] 2012 Xie et al. [42] 2012 C. Yang et al. [43] 2013 Wang et al. [41] 2013 S. Lee et al. [26,27] 2”
Structure-Based Sybil Detection in Social Networks via Local Rule-Based Propagation (2018, methodology): “SybilRank [8] uses short RWs to distribute benignness scores from a set of labeled benign users to all the remaining users.”
The rise of social bots (2014, background): “The ability to extrapolate such information will allow us to systematically infer who are the entities behind them.”
Strength in Numbers: Robust Tamper Detection in Crowd Computations (2015, background): “Existing Sybil defenses have mostly taken the approach of detecting individual Sybils [15, 36, 37, 41], enabling the operator to either sus-pend the Sybils or nullify their contribution to the crowd computation.”
Random Walk Based Fake Account Detection in Online Social Networks (2017, methodology): “Example random walk based methods include SybilGuard [6], SybilLimit [7], SybilInfer [8], SybilRank [10], Criminal account Inference Algorithm (CIA) [11], and”
Combating Attacks and Abuse in Large Online Communities (2016, background): “In the context of online social networks, most Sybil detection systems rely on social graphs [247, 246, 217, 221, 69, 58].”
SoK: The Evolution of Sybil Defense via Social Networks (2013, methodology): “This compelling vision, first articulated in the SybilGuard protocol [46], is further refined in their later work on SybilLimit [45] and has inspired several other efforts in sybil defense [7], [10], [35], [42].”
Defending against Sybil Devices in Crowdsourced Mapping Services (2015, methodology): “Among available algorithms, we use SybilRank [10].”
SybilBlind: Detecting Fake Users in Online Social Networks without Manual Labels (2018, methodology): “3) SybilBlind outperforms SybilRank and is comparable with SybilBelief, even if SybilRank and SybilBelief use a labeled training dataset.”
SybilBelief: A Semi-Supervised Learning Approach for Structure-Based Sybil Detection (2013, methodology): “[13] showed that SybilRank outperforms them in terms of rankings.”
SybilSCAR: Sybil detection in online social networks via local rule based propagation (2017, methodology): “[8] found that Tuenti, the largest OSN in Spain, satisfies the homophily assumption, and SybilRank can detect a large amount of Sybils in Tuenti.”