Using supervised machine learning algorithms to detect suspicious URLs in online social networks-Reference-Cited by-同舟云学术

Using supervised machine learning algorithms to detect suspicious URLs in online social networks

Published:2017-07-31 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017
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Author:

Al-Janabi Mohammed¹,Quincey Ed de¹,Andras Peter¹

Affiliation:

1. Keele University

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3110025.3116201

Reference42 articles.

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2. Facebook immune system

3. The Koobface botnet and the rise of social malware

4. S. Sheng B. Wardman G. Warner L. F. Cranor J. Hong and C. Zhang "An Empirical Analysis of Phishing Blacklists " 2009. S. Sheng B. Wardman G. Warner L. F. Cranor J. Hong and C. Zhang "An Empirical Analysis of Phishing Blacklists " 2009.

5. C. Grier K. Thomas V. Paxson and M. Zhang "@spam: The Underground on 140 Characters or Less " in Proceedings of the 17th ACM conference on Computer and communications security - CCS '10 2010 p. 27. C. Grier K. Thomas V. Paxson and M. Zhang "@spam: The Underground on 140 Characters or Less " in Proceedings of the 17th ACM conference on Computer and communications security - CCS '10 2010 p. 27.

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