# Distributing Intelligence in 6G Programmable Data Planes for Effective In-Network Intrusion Prevention

arXiv: https://arxiv.org/abs/2410.24013 · HTML: https://mlsanity.com/paper/2410.24013/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Oct 2024.

## Built on by (most cited first, 1 of 1)

- [Optimal In-Network Distribution of Learning Functions for a Secure-by-Design Programmable Data Plane of Next-Generation Networks](https://mlsanity.com/paper/2411.18384/) (2024)

## Abstract (arXiv)

The problem of attacks on new generation network infrastructures is becoming increasingly relevant, given the widening of the attack surface of these networks resulting from the greater number of devices that will access them in the future (sensors, actuators, vehicles, household appliances, etc.). Approaches to the design of intrusion detection systems must evolve and go beyond the traditional concept of perimeter control to build on new paradigms that exploit the typical characteristics of future 5G and 6G networks, such as in-network computing and intelligent programmable data planes. The aim of this research is to propose a disruptive paradigm in which devices in a typical data plane of a future programmable network have anomaly detection capabilities and cooperate in a fully distributed fashion to act as an ML-enabled Intrusion Prevention System ``embedded" into the network. The reported proof-of-concept experiments demonstrate that the proposed paradigm allows working effectively and with a good level of precision while occupying overall less CPU and RAM resources of the devices involved.