CANTINA+: A Feature-Rich Machine Learning Framework for Detecting Phishing Web Sites

@article{Xiang2011CANTINAAF,
  title={CANTINA+: A Feature-Rich Machine Learning Framework for Detecting Phishing Web Sites},
  author={Guang Xiang and J. Hong and C. Ros{\'e} and L. Cranor},
  journal={ACM Trans. Inf. Syst. Secur.},
  year={2011},
  volume={14},
  pages={21:1-21:28}
}
  • Guang Xiang, J. Hong, +1 author L. Cranor
  • Published 2011
  • Computer Science
  • ACM Trans. Inf. Syst. Secur.
  • Phishing is a plague in cyberspace. Typically, phish detection methods either use human-verified URL blacklists or exploit Web page features via machine learning techniques. However, the former is frail in terms of new phish, and the latter suffers from the scarcity of effective features and the high false positive rate (FP). To alleviate those problems, we propose a layered anti-phishing solution that aims at (1) exploiting the expressiveness of a rich set of features with machine learning to… CONTINUE READING
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    References

    SHOWING 1-2 OF 2 REFERENCES
    Gartner survey
    • Gartner survey
    • 2007
    A Tutorial on Support Vector Machines for Pattern Recognition
    • 12,894
    • Highly Influential
    • PDF