Algorithms on strings, trees, and sequences: computer science and computational biology
Algorithms on strings, trees, and sequences: computer science and computational biology
Communications of the ACM - Ontology: different ways of representing the same concept
Robust Hyperlinks Cost Just Five Words Each
Robust Hyperlinks Cost Just Five Words Each
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Do security toolbars actually prevent phishing attacks?
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Cantina: a content-based approach to detecting phishing web sites
Proceedings of the 16th international conference on World Wide Web
Why Johnny can't encrypt: a usability evaluation of PGP 5.0
SSYM'99 Proceedings of the 8th conference on USENIX Security Symposium - Volume 8
Phishing defense against IDN address spoofing attacks
Proceedings of the 2006 International Conference on Privacy, Security and Trust: Bridge the Gap Between PST Technologies and Business Services
Does domain highlighting help people identify phishing sites?
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
CANTINA+: A Feature-Rich Machine Learning Framework for Detecting Phishing Web Sites
ACM Transactions on Information and System Security (TISSEC)
Communications of the ACM
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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.