Social phishing

By Tom N. Jagatic, N. Johnson, Markus Jakobsson, Filippo Menczer. 2007

621 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

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Citations in the corpus

PhishEye: Live Monitoring of Sandboxed Phishing Kits (2016, background): “first report a baseline success rate for individual phishing emails [17], while Jakobsson et al.”
All your contacts are belong to us: automated identity theft attacks on social networks (2009, positive): “In [20], the authors present experiments that they have performed on “social phishing”.”
Arming the public with artificial intelligence to counter social bots (2019, background): “Unlike other social engineering attacks, such as spear phishing (Jagatic et al., 2007), bots can achieve scalability through automation.”
Opt out of privacy or "go home": understanding reluctant privacy behaviours through the FoMO-centric design paradigm (2019, positive): ““Social” phishing attempts using data mined through social media were found to be 67% e � ective, compared to the 16% e � ectiveness of regular phishing [38].”
Phishing Attacks: A Recent Comprehensive Study and a New Anatomy (2021, background): “Generally, most of the phishing attacks start with an email (Jagatic et al., 2007).”
Financial Fraud: A Literature Review (2018, neutral): “The most common social engineering scheme technique is what is called phishing (see Lynch, 2005; Jagatic et al. , 2007).”
Handcrafted Fraud and Extortion: Manual Account Hijacking in the Wild (2014, background): “Although phishing as an attack mechanism is well studied [1, 6, 16, 27, 13], little is known about its victims or effectiveness.”
Uncovering social spammers: social honeypots + machine learning (2010, background): “In particular, social spammers are increasingly targeting these systems as part of phishing attacks [14], to disseminate malware [5] and commercial spam messages [7, 26], and to promote affiliate websites [17].”
The spread of low-credibility content by social bots (2017, neutral): “Bots can tailor misinformation and target those who are most likely to believe it, taking advantage of our tendencies to attend to what appears popular, to trust information in a social setting [25], and to trust social contacts [23].”
EASiER: encryption-based access control in social networks with efficient revocation (2011, background): “duces a privacy risk, as the provider is an attractive attack target [6].”