  The advent of Programmable Data Planes represents an outstanding evolution
and complete revolution of the Software- Defined Networking paradigm. The
capacity to define the entire behavior of forwarding devices by controlling the
packet parsing procedures and executing custom operations enables offloading
functionalities traditionally performed at the control plane. A recent research
line has explored the possibility of even offloading to the data plane part of
Artificial Intelligence algorithms, and more specifically, Machine Learning
ones, to increase their accuracy and responsiveness (by having more detailed
visibility of the traffic). This introduces a significant opportunity for
evolution in the critical field of Intrusion Detection. However, offloading
functionalities to the data plane is not a straightforward task. In this paper,
we discuss how Programmable Data Planes might complement different stages of an
Intrusion Detection System based on Machine Learning. We present two use cases
that make evident the feasibility of this approach and highlight aspects that
must be considered when addressing the challenge of deploying solutions
leveraging data-plane functionalities.
