  Network intrusion detection is one of the most visible uses for Big Data
analytics. One of the main problems in this application is the constant rise of
new attacks. This scenario, characterized by the fact that not enough labeled
examples are available for the new classes of attacks is hardly addressed by
traditional machine learning approaches. New findings on the capabilities of
Zero-Shot learning (ZSL) approach makes it an interesting solution for this
problem because it has the ability to classify instances of unseen classes. ZSL
has inherently two stages: the attribute learning and the inference stage. In
this paper we propose a new algorithm for the attribute learning stage of ZSL.
The idea is to learn new values for the attributes based on decision trees
(DT). Our results show that based on the rules extracted from the DT a better
distribution for the attribute values can be found. We also propose an
experimental setup for the evaluation of ZSL on network intrusion detection
(NID).
