EVILCOHORT: Detecting Communities of Malicious Accounts on Online Services

By Gianluca Stringhini, Pierre Mourlanne, G. Jacob, Manuel Egele, Christopher Krügel, Giovanni Vigna. 2015

84 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Minghong Fang
2. Neil Zhenqiang Gong
3. Guolei Yang
4. Jia Liu
5. Sebastian Szyller
6. Buse Gul Atli
7. Samuel Marchal
8. N. Asokan

Citations in the corpus

• Poisoning Attacks to Graph-Based Recommender Systems (2018, methodology): “Detecting such fake users is also known as Sybil detection , and many methods (e.g., [8, 28, 32]) have been developed to detect fake users.”
• DAWN: Dynamic Adversarial Watermarking of Neural Networks (2019, background): “umber or credit card, to mitigate this threat. Also, Sybils-detection techniques exist [43, 46] and it is possible to link Sybils accounts by examining querying patterns and IP addresses for instance [40]. For example, to protect Caltech-RN34, we could increase rw to reliably verify the watermark of”