33 citations · 106 across the 8 of their papers we have counts for
7 papers
Sharing FANCI Features: A Privacy Analysis of Feature Extraction for DGA Detection
Benedikt Holmes, Arthur Drichel, Ulrike Meyer
The goal of Domain Generation Algorithm (DGA) detection is to recognize infections with bot malware and is often done with help of Machine Learning approaches that classify non-res…
The More, the Better? A Study on Collaborative Machine Learning for DGA Detection
Arthur Drichel, Benedikt Holmes, Justus von Brandt +1
Domain generation algorithms (DGAs) prevent the connection between a botnet and its master from being blocked by generating a large number of domain names. Promising single-data-so…
Finding Phish in a Haystack: A Pipeline for Phishing Classification on Certificate Transparency Logs
Arthur Drichel, Vincent Drury, Justus von Brandt +1
Current popular phishing prevention techniques mainly utilize reactive blocklists, which leave a ``window of opportunity'' for attackers during which victims are unprotected. One p…
First Step Towards EXPLAINable DGA Multiclass Classification
Arthur Drichel, Nils Faerber, Ulrike Meyer
Numerous malware families rely on domain generation algorithms (DGAs) to establish a connection to their command and control (C2) server. Counteracting DGAs, several machine learni…
Making Use of NXt to Nothing: The Effect of Class Imbalances on DGA Detection Classifiers
Arthur Drichel, Ulrike Meyer, Samuel Schüppen +1
Numerous machine learning classifiers have been proposed for binary classification of domain names as either benign or malicious, and even for multiclass classification to identify…
Analyzing the Real-World Applicability of DGA Classifiers
Arthur Drichel, Ulrike Meyer, Samuel Schüppen +1
Separating benign domains from domains generated by DGAs with the help of a binary classifier is a well-studied problem for which promising performance results have been published.…