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

Abstract

A recent research line has explored the possibility of leveraging functionalities of Programmable Data Planes to offload part of Machine Learning algorithms to the data plane, which might contribute to increase their accuracy and responsiveness by having a more detailed visibility of the traffic. This approach introduces a significant opportunity for evolution in the critical field of Intrusion Detection. 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 non straightforward task of deploying solutions leveraging data-plane functionalities.