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The recent evolution of Internet to new paradigms such as network function virtualization and software defined networking poses new relevant challenges to the detection of Botnet attacks, calling for innovative approaches. In this work we propose a detection mechanism based on an Artificial Neural Net classifier trained by available data sets collected in conventional networks. We apply such detection mechanism to the timely use case scenario of a software defined network infected by the dangerous Botnet Mirai, circulating in October 2016. Experimental results show an accuracy of Botnet detection higher than 99%, thus outperforming available Botnet detection mechanisms currently used in conventional networks.