On detecting and mitigating phishing attacks through featureless machine learning techniques

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.