# PhishZip: A New Compression-based Algorithm for Detecting Phishing Websites

arXiv: https://arxiv.org/abs/2007.11955 · HTML: https://mlsanity.com/paper/2007.11955/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jul 2020.

## Built on by (most cited first, 2 of 2)

- [PhishSim: Aiding Phishing Website Detection with a Feature-Free Tool](https://mlsanity.com/paper/2207.10801/) (2022)
- [Detecting Phishing Sites -- An Overview](https://mlsanity.com/paper/2103.12739/) (2021)

## Abstract (arXiv)

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%).