Verifying phishmon
John Tomaselli, Austin Willoughby, Jorge Vargas Amezcua, Emma Delehanty, Katherine Floyd, Damien Wright, Mark Lammers, Ron Vetter · 2021
Phishing attacks are the scourge of the network security manager's job. Looking for a solution to counter this trend, this paper examines and verifies the efficacy of Phishmon, a machine learning framework for scrutinizing webpages that relies on technical attributes of the webpage's structure for classification. More specifically, each of the four machine learning algorithms mentioned in the original paper are applied to a portion of the data set used by Phishmon's creators in order to verify and confirm their results.