Photonic-aware Neural Networks for Packet Classification in Beyond 5G Networks

Abstract

The benefits introduced by novel network technologies such as 5G and beyond, including low latency and support for billions of devices, have the potential to transform the lives of people. However, the features promised by these new technologies have also attracted malicious actors, with various motivations for attacking the network infrastructure, from cybercrime-based frauds to political goals. Thus, to enable the full potential of the emerging network technologies, it is necessary to take into accounts these attacks and develop tailored countermeasures. One future direction in mitigating the risks of potential attacks is the automatic classification of malicious packets, with the possibility to drop them if classified in the attack category. Hence, in this context, we propose a solution based on Neural Networks (NNs) to automatically classify packets into two classes, i.e., benign and attack, directly in the Radio Access Network (RAN), specifically inspecting packets when they are relayed at the next generation eNB (gNB)-Central Unit (CU) level. Since NNs can be computationally intensive algorithms, potentially increasing the latency of the network, we decide to leverage Photonic-Aware Neural Network (PANN), photonic accelerators able to perform NN computations in the analog optical domain and with time-of-flight latency. We devised two different PANN architectures, considering different photonic constraints. The classification performance of the two architectures has been assessed on the CICIDS-2017 dataset and compared with electronic counterparts. Results proved that the F1-score loss due to underlying hardware constraints is negligible, paving the way for PANN applications in next generation networks.