  In this paper, we propose a feature-free method for detecting phishing
websites using the Normalized Compression Distance (NCD), a parameter-free
similarity measure which computes the similarity of two websites by compressing
them, thus eliminating the need to perform any feature extraction. It also
removes any dependence on a specific set of website features. This method
examines the HTML of webpages and computes their similarity with known phishing
websites, in order to classify them. We use the Furthest Point First algorithm
to perform phishing prototype extractions, in order to select instances that
are representative of a cluster of phishing webpages. We also introduce the use
of an incremental learning algorithm as a framework for continuous and adaptive
detection without extracting new features when concept drift occurs. On a large
dataset, our proposed method significantly outperforms previous methods in
detecting phishing websites, with an AUC score of 98.68%, a high true positive
rate (TPR) of around 90%, while maintaining a low false positive rate (FPR) of
0.58%. Our approach uses prototypes, eliminating the need to retain long term
data in the future, and is feasible to deploy in real systems with a processing
time of roughly 0.3 seconds.
