no code implementations • 13 Jul 2022 • Rizka Purwanto, Arindam Pal, Alan Blair, Sanjay Jha
This method examines the HTML of webpages and computes their similarity with known phishing websites, in order to classify them.
no code implementations • 27 Aug 2021 • Rizka Purwanto, Arindam Pal, Alan Blair, Sanjay Jha
Our paper compares the performances of six well-known, state-of-the-art AutoML frameworks on ten different phishing datasets to see whether AutoML-based models can outperform manually crafted machine learning models.
no code implementations • 22 Jul 2020 • Rizka Purwanto, Arindam Pal, Alan Blair, Sanjay Jha
PhishZip outperforms the use of best-performing HTML-based features in past studies, with a true positive rate of 80. 04%.