Phishing has grown significantly in the past few years and is predicted to further increase in the future. The dynamics of phishing introduce challenges in implementing a robust phishing detection system and selecting features which can represent phishing despite the change of attack. In this paper, we propose PhishZip which is a novel phishing detection approach using a compression algorithm to perform website classification and demonstrate a systematic way to construct the word dictionaries for the compression models using word occurrence likelihood analysis. PhishZip outperforms the use of best-performing HTML-based features in past studies, with a true positive rate of 80.04%. We also propose the use of compression ratio as a novel machine learning feature which significantly improves machine learning based phishing detection over previous studies. Using compression ratios as additional features, the true positive rate significantly improves by 30.3% (from 51.47% to 81.77%), while the accuracy increases by 11.84% (from 71.20% to 83.04%).

本文提出PhishZip，一种新的钓鱼检测方法，使用压缩算法进行网站分类，并演示了使用单词出现似然分析构建压缩模型单词词典的系统方法。使用压缩比作为机器学习特征，PhishZip检测效果优于先前研究中表现最佳的基于HTML的特征，真正阳性率可达80.04％。同时，使用压缩比作为附加特征，真正阳性率显着提高30.3％（从51.47％提高到81.77％） ，准确性提高11.84％（从71.20％提高到83.04％）。

PhishZip：一种基于压缩的检测网络钓鱼网站的新算法