Machine Learning 5G Attack Detection in Programmable Logic

Abstract

Machine learning-assisted network security may significantly contribute to securing 5G components. However, machine learning network security inference generally requires tens to hundreds of milliseconds, thereby introducing significant latency in 5G operations. The inference latency can be reduced by deploying the machine learning model to programmable logic in a field programmable gate array at the cost of a small loss in accuracy. In order to quantify this loss, as well as to establish baseline performance inference latency for programmable logic implementations, this work explores an autoencoder and a beta-variational autoencoder deployed on two different field programmable gate array evaluation boards and compares accuracy and performance against an NVIDIA A100 graphics processing unit implementation. A publicly available 5G dataset containing 10 types of attacks along with normal traffic is introduced as part of the evaluation.