Sophisticated Phishers Make More Spelling Mistakes: Using URL Similarity against Phishing

Abstract

Phishing attacks rise in quantity and quality. With short online lifetimes of those attacks, classical blacklist based approaches are not sufficient to protect online users. While attackers manage to achieve high similarity between original and fraudulent websites, this fact can also be used for attack detection. In many cases attackers try to make the Internet address (URL) from a website look similar to the original. In this work, we present a way of using the URL itself for automated detection of phishing websites by extracting and verifying different terms of a URL using search engine spelling recommendation.

We evaluate our concept against a large test set of 8730 real phishing URLs. In addition, we collected scores for the visual quality of a subset of those attacks to be able to compare the performance of our tests for different attack qualities. Results suggest that our heuristics are able to mark 54.3% of the malicious URLs as suspicious. With increasing visual quality of the phishing websites, the number of URL characteristics that allow a detection increases, as well.