Making use of NXt to nothing

International Conference on Availability, Reliability and Security (ARES), pp. 1–9

Abstract

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 the domain generation algorithm (DGA) that generated a specific domain name. Both classification tasks have to deal with the class imbalance problem of strongly varying amounts of training samples per DGA. Currently, it is unclear whether the inclusion of DGAs for which only a few samples are known to the training sets is beneficial or harmful to the overall performance of the classifiers. In this paper, we perform a comprehensive analysis of various contextless DGA classifiers, which reveals the high value of a few training samples per class for both classification tasks. We demonstrate that the classifiers are able to detect various DGAs with high probability by including the underrepresented classes which were previously hardly recognizable. Simultaneously, we show that the classifiers' detection capabilities of well represented classes do not decrease.

Authors 4

1. Arthur Drichel Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University

2. Ulrike Meyer Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University

3. Samuel Schüppen
   Affiliation as printed

   Siemens CERT

4. Dominik Teubert
   Affiliation as printed

   Siemens CERT