钓鱼网站数据集

Rami Mustafa A Mohammad ( University of Huddersfield, rami.mohammad '@' hud.ac.uk, rami.mustafa.a '@' gmail.com) Lee McCluskey (University of Huddersfield,t.l.mccluskey '@' hud.ac.uk ) Fadi Thabtah (Canadian University of Dubai,fadi '@' cud.ac.ae) Data Set Information: 我们研究面临的挑战之一是无法获得可靠的培训数据集。事实上，该领域的任何研究人员都面临着这一挑战。然而，尽管最近发布了大量关于预测网络钓鱼网站的文章，但没有公开发布可靠的培训数据集，这可能是因为文献中对网络钓鱼网页的最终特征没有达成一致意见，因此，很难形成覆盖所有可能特征的数据集。 在这个数据集中，我们阐明了在预测网络钓鱼网站方面被证明是可靠和有效的重要特征。此外，我们还提出了一些新特性。 Attribute Information: For Further information about the features see the features file in the data folder. Relevant Papers: Mohammad, Rami, McCluskey, T.L. and Thabtah, Fadi (2012) An Assessment of Features Related to Phishing Websites using an Automated Technique. In: International Conferece For Internet Technology And Secured Transactions. ICITST 2012 . IEEE, London, UK, pp. 492-497. ISBN 978-1-4673-5325-0 Mohammad, Rami, Thabtah, Fadi Abdeljaber and McCluskey, T.L. (2014) Predicting phishing websites based on self-structuring neural network. Neural Computing and Applications, 25 (2). pp. 443-458. ISSN 0941-0643 Mohammad, Rami, McCluskey, T.L. and Thabtah, Fadi Abdeljaber (2014) Intelligent Rule based Phishing Websites Classification. IET Information Security, 8 (3). pp. 153-160. ISSN 1751-8709 Citation Request: Please refer to the Machine Learning Repository's citation policy