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The advent of Programmable Data Planes represents an outstanding evolutionand complete revolution of the Software- Defined Networking paradigm. Thecapacity to define the entire behavior of forwarding devices by controlling thepacket parsing procedures and executing custom operations enables offloadingfunctionalities traditionally performed at the control plane. A recent researchline has explored the possibility of even offloading to the data plane part ofArtificial Intelligence algorithms, and more specifically, Machine Learningones, to increase their accuracy and responsiveness (by having more detailedvisibility of the traffic). This introduces a significant opportunity forevolution in the critical field of Intrusion Detection. However, offloadingfunctionalities to the data plane is not a straightforward task. In this paper,we discuss how Programmable Data Planes might complement different stages of anIntrusion Detection System based on Machine Learning. We present two use casesthat make evident the feasibility of this approach and highlight aspects thatmust be considered when addressing the challenge of deploying solutionsleveraging data-plane functionalities.

Watching Smartly from the Bottom: Intrusion Detection revamped through Programmable Networks and Artificial Intelligence