Distributing Intelligence in 6G Programmable Data Planes for Effective In-Network Deployment of an Active Intrusion Detection System

• URL: http://arxiv.org/abs/2410.24013v1
• Date: Thu, 31 Oct 2024 15:14:15 GMT
• Title: Distributing Intelligence in 6G Programmable Data Planes for Effective In-Network Deployment of an Active Intrusion Detection System
• Authors: Mattia G. Spina, Floriano De Rango, Edoardo Scalzo, Francesca Guerriero, Antonio Iera,
• Abstract summary: This research aims to propose a disruptive paradigm in which devices in a typical data plane of a future programmable network have %classification and anomaly detection capabilities and cooperate in a fully distributed fashion to act as an ML-enabled Active Intrusion Detection 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.
• Score: 2.563180814294141
• License:
• Abstract: 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 %classification and anomaly detection capabilities and cooperate in a fully distributed fashion to act as an ML-enabled Active Intrusion Detection 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.

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