Understanding fraudulent activities in online ad exchanges
By Brett Stone-Gross, Ryan Stevens, Apostolis Zarras, Richard A. Kemmerer, Christopher Krügel, Giovanni Vigna. 2011
129 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Networks and Communications, Information Systems
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Giovanni Vigna
- Weinan Zhang
- Jun Wang
- Christopher Krügel
- Shuai Yuan
- Gianluca Stringhini
- Fabrizio Silvestri
- Andrew Haines
In Computer Networks and Communications
- Giovanni Vigna
- Christopher Krügel
- Weinan Zhang
- Ben Y. Zhao
- Jun Wang
- Shuai Yuan
- Haitao Zheng
- Fabrizio Silvestri
In Information Systems
- Shuai Yuan
- Giovanni Vigna
- Weinan Zhang
- Jun Wang
- Christopher Krügel
- Gianluca Stringhini
- Ben Y. Zhao
- Fabrizio Silvestri
Citations in the corpus
- Interpretable Predictions of Tree-based Ensembles via Actionable Feature Tweaking (2017, background): “, number of ad impressions, click-through rate or CTR, bounce rate) as well as effective mechanisms to overcome fraudulent activities such as click spam [7, 22].”
- Serf and turf: crowdturfing for fun and profit (2011, background): “01) for traditional display advertising on the web [30].”
- Display Advertising with Real-Time Bidding (RTB) and Behavioural Targeting (2016, methodology): “…al., 2012, Zhang et al., 2016a], knowledge extraction [Ahmed et al., 2011, Yan et al., 2009], relevance feedback [Chapelle, 2014], fraud detection [Stone-Gross et al., 2011, Alrwais et al., 2012, Crussell et al., 2014, Stitelman et al., 2013], incentives and economics [Balseiro 1According to…”
- Shady paths: leveraging surfing crowds to detect malicious web pages (2013, background): “For this page, the full URL is U , D is evil.com, L is 4, TLD is .com, Pg is search.php, Pa is (q,id), and the IP address returned by the DNS server for evil.com is I.”