Detecting Unknown DGAs without Context Information

no code implementations • 30 May 2022 • Arthur Drichel, Justus von Brandt, Ulrike Meyer

While binary classifiers can label domains of yet unknown DGAs as malicious, multiclass classifiers can only assign domains to DGAs that are known at the time of training, limiting the ability to uncover new malware families.

Attribute Binary Classification +4

The More, the Better? A Study on Collaborative Machine Learning for DGA Detection

no code implementations • 24 Sep 2021 • Arthur Drichel, Benedikt Holmes, Justus von Brandt, Ulrike Meyer

In this paper, we complement the research area of DGA detection by conducting a comprehensive collaborative learning study, including a total of 13, 440 evaluation runs.

Finding Phish in a Haystack: A Pipeline for Phishing Classification on Certificate Transparency Logs

1 code implementation • 23 Jun 2021 • Arthur Drichel, Vincent Drury, Justus von Brandt, Ulrike Meyer

In this paper, we present a pipeline that facilitates such evaluations by addressing a number of problems when working with CT log data.