On detecting and mitigating phishing attacks through featureless machine learning techniques
Cristian H. M. Souza, Marcilio O. O. Lemos, Felipe S. Dantas Silva, Robinson Alves · Internet Technology Letters · 2019
The expansion of the Internet has grown the possibilities for fraudulent actions. Among these possibilities, we highlight the phishing activity, created with the objective of capturing user's credentials through a false page similar to the original one. This work proposes PhishKiller, a tool capable of detecting and mitigating phishing attacks by means a proxy approach employed to intercept user‐accessed addresses, and featureless machine learning techniques to classify URLs. The proof‐of‐concept evaluation results revealed that PhishKiller has a more cost‐effective compared to state of the art, with an accuracy of 98.30% and taking only 81.68 ms to predict and block malicious websites.