PhishSim: Aiding Phishing Website Detection with a Feature-Free Tool

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.

Incremental Learning Phishing Website Detection

Man versus Machine: AutoML and Human Experts' Role in Phishing Detection

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.

AutoML BIG-bench Machine Learning

PhishZip: A New Compression-based Algorithm for Detecting Phishing Websites

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%.

BIG-bench Machine Learning