Facebook immune system
Abstract
Popular Internet sites are under attack all the time from phishers, fraudsters, and spammers. They aim to steal user information and expose users to unwanted spam. The attackers have vast resources at their disposal. They are well-funded, with full-time skilled labor, control over compromised and infected accounts, and access to global botnets. Protecting our users is a challenging adversarial learning problem with extreme scale and load requirements. Over the past several years we have built and deployed a coherent, scalable, and extensible realtime system to protect our users and the social graph. This Immune System performs realtime checks and classifications on every read and write action. As of March 2011, this is 25B checks per day, reaching 650K per second at peak. The system also generates signals for use as feedback in classifiers and other components. We believe this system has contributed to making Facebook the safest place on the Internet for people and their information. This paper outlines the design of the Facebook Immune System, the challenges we have faced and overcome, and the challenges we continue to face.
Integrity signals
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Each is checked against this paper’s own text: does it say what the citing paper says it does?
The paper found rapid review bursts in both qualitative and quantitative investigations, consistent with assumptions in prior fraud-detection work, and interview participants revealed adaptive rate control as an avoidance technique.
“We found ASO workers who post fake reviews in rapid bursts in both our qualitative and quantitative investigations.This is consistent with assumptions made in previous fraud detection work, e.g., [26, 28, 32, 36-38, 40, 42, 43, 51, 52, 59-61, 63, 65, 92, 95, 96, 100].”
From The Art and Craft of Fraudulent App Promotion in Google Play (2019) · cites this paper as [51]
Several participants admitted to reusing common linguistic patterns and copying reviews across similar products, and the quantitative study confirmed this.
“several participants admitted to reuse common linguistic patterns and copy reviews across similar products.”
From The Art and Craft of Fraudulent App Promotion in Google Play (2019) · cites this paper as [51]
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- Authors
- Stein, T., Chen, E., & Mangla, K.
- Affiliations
- Meta (United Kingdom) (GB)
- Venue
- Proceedings of the 4th Workshop on Social Network Systems
- Publisher
- ACM
- Published
- 2011-04-10
- Pages
- 1-8
- OpenAlex
- W2156836300
- Cited by
- 219
- References
- 11
- Citations by year
- 2026: 1 · 2024: 3 · 2023: 8 · 2022: 11 · 2021: 9 · 2020: 22 · 2019: 13 · 2018: 27 · 2017: 22 · 2016: 24 · 2015: 25 · 2014: 20 · 2013: 10 · 2012: 19
- FWCI
- 12.97
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Social Network Systems (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
- Keywords
- phishing detection, fraud detection, spam detection, real-time classification, botnets
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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