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We present a testbed implementation for the development, evaluation and demonstration of security orchestration
in a network function virtualization environment. As a specific
scenario, we demonstrate how an intelligent response to DDoS
and various other kinds of targeted attacks can be formulated
such that these attacks and future variations can be mitigated. We utilise machine learning to characterise normal network traffic, attacks and responses, then utilise this information to orchestrate virtualized network functions around affected components to isolate these components and to capture, redirect and filter traffic (e.g. honeypotting) for additional analysis. This allows us to maintain a high level of network quality of service to
given network functions and components despite adverse network conditions.