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Numerous machine learning classifiers have been proposed for binaryclassification of domain names as either benign or malicious, and even formulticlass classification to identify the domain generation algorithm (DGA)that generated a specific domain name. Both classification tasks have to dealwith the class imbalance problem of strongly varying amounts of trainingsamples per DGA. Currently, it is unclear whether the inclusion of DGAs forwhich only a few samples are known to the training sets is beneficial orharmful to the overall performance of the classifiers. In this paper, weperform a comprehensive analysis of various contextless DGA classifiers, whichreveals the high value of a few training samples per class for bothclassification tasks. We demonstrate that the classifiers are able to detectvarious DGAs with high probability by including the underrepresented classeswhich were previously hardly recognizable. Simultaneously, we show that theclassifiers' detection capabilities of well represented classes do notdecrease.

Making Use of NXt to Nothing: The Effect of Class Imbalances on DGA Detection Classifiers