Detecting and characterizing social spam campaigns

By Hongyu Gao, Jun Hu, Christo Wilson, Zhichun Li, Yan Chen, Ben Y. Zhao. Conference on Computer and Communications Security, 2010

488 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Information Systems

View Paper: https://www.semanticscholar.org/paper/437217438cc03ab106e42e9851d4e2930d8175d1

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Citations in the corpus

Analyzing spammers' social networks for fun and profit: a case study of cyber criminal ecosystem on twitter (2012, background): “present a study on detecting and characterizing social spam campaigns in Facebook [21].”
Empirical Evaluation and New Design for Fighting Evolving Twitter Spammers (2011, background): “Gao et al. [23] presented a study on detecting and characterizing social spam campaigns.”
Understanding latent interactions in online social networks (2010, background): “Finally, a recent study from Northwestern and UC Santa Barbara quantified the role of spam and phishing attacks in Facebook wall posts [Gao et al. 2010].”
Combating Attacks and Abuse in Large Online Communities (2016, methodology): “As of 2015, Facebook has conservatively estimated that there are at least 170 million Sybils on their service, counting for almost 10% of the Facebook population [27].”
Loose tweets: an analysis of privacy leaks on twitter (2011, unknown): “analyze URL-based spam in social networks [8]).”
Handcrafted Fraud and Extortion: Manual Account Hijacking in the Wild (2014, background): “Criminals leverage millions of hijacked credentials to send spam [5, 10], tap into the social connections of victim’s to compromise additional accounts [24], or alternatively liquidate a victim’s financial assets using malware such as Zeus or SpyEye [12].”
Adapting Social Spam Infrastructure for Political Censorship (2012, background): “Similarly, an underground marketplace has emerged that facilitates purchasing social network accounts and URL advertisements [23].”
Man vs. Machine: Practical Adversarial Detection of Malicious Crowdsourcing Workers (2014, background): “Researchers have developed mechanisms to detect fake accounts (Sybil) and spam campaigns in online social networks, including Facebook [15, 49], Twitter [43], Renren [52] and LinkedIn [46].”
Uncovering social network sybils in the wild (2011, methodology): “Prior work has shown that these strategies are widely used by attackers against other OSNs [Gao et al. 2010; Grier et al. 2010; Thomas et al. 2011].”
Towards Detecting Anomalous User Behavior in Online Social Networks (2014, methodology): “Techniques used to address this problem to date have focused primarily on detecting specific attack strategies, for example, detecting Sybil accounts [10, 65, 67], or detecting coordinated posting of content [36].”
Trafficking Fraudulent Accounts: The Role of the Underground Market in Twitter Spam and Abuse (2013, background): “With no central operation of the underground market, we resort to investigating common haunts: advertisements via search engines, blackhat forums such as blackhat-world.com , and freelance labor pages including Fiverr and Freelancer [20, 21].”
Aiding the Detection of Fake Accounts in Large Scale Social Online Services (2012, background): “For instance, they enable spammers to abuse an OSN’s messaging system to post spam [28, 51], or waste an OSN advertising”
Understanding user behavior in online social networks: a survey (2013, background): “Gao et al. [17] study a large dataset composed of over 187 million wall messages among 3.5 million Facebook users.”
EVILCOHORT: Detecting Communities of Malicious Accounts on Online Services (2015, background): “On the other hand, E VIL C OHORT can detect an account (or an IP address) as malicious even if the account does not post any content on the online service.”
COMPA: Detecting Compromised Accounts on Social Networks (2013, background): “COMPA was able to identify compromised accounts on both social networks with high precision.”
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].”