  Efficient network management is one of the key challenges of the constantly
growing and increasingly complex wide area networks (WAN). The paradigm shift
towards virtualized (NFV) and software defined networks (SDN) in the next
generation of mobile networks (5G), as well as the latest scientific insights
in the field of Artificial Intelligence (AI) enable the transition from
manually managed networks nowadays to fully autonomic and dynamic
self-organized networks (SON). This helps to meet the KPIs and reduce at the
same time operational costs (OPEX). In this paper, an AI driven concept is
presented for the malfunction detection in NFV applications with the help of
semi-supervised learning. For this purpose, a profile of the application under
test is created. This profile then is used as a reference to detect abnormal
behaviour. For example, if there is a bug in the updated version of the app, it
is now possible to react autonomously and roll-back the NFV app to a previous
version in order to avoid network outages.
