A trust distributed learning (D‐NWDAF) against poisoning and byzantine attacks in B5G networks

Abstract

AbstractBeyond 5G networks, (B5G) introduce the emergence of a smart service and an automate management archetype of running network slices, such as the network data analytics function (NWDAF). These new architectures provide a large usage of advanced machine learning algorithms, in order to dynamically build efficient decisions. In this perspective, the Distributed Learning (D‐), known as A Distributed NWDAF Architecture (D‐NWDAF) has proved its efficiency in not only building collaborative deep learning models, among several network slices, but also ensuring the privacy and isolation of such network slices. Notwithstanding, FL is vulnerable to different attacks, where FL nodes (Leaf NWDAF: AI‐VNFs*) may upload malicious updates to the FL central (root NWDAF) entity so that it can cause a construction failure of the FL global model and affect the global performance. Moreover, attacks detection of FL‐based solutions give “black‐box” decisions about the performance of running network slices and the attacks detected. In other words, detection solutions do not provide any details about why and how such ML attacks decisions were made. Thus, such decisions cannot be properly trusted and comprehended by B5G slice managers. To resolve this issue, we leverage Dimensional Reduction (DR) and Explainable Artificial Intelligence (XAI) algorithms which aim to detect attacks and improve the transparency of black‐box FL attacks detection‐making process. In the present article, we design a novel DR‐XAI‐powered framework to detect attacks and explain the FL‐based attacks detection. We first build a deep learning model in a federated way, to predict key performance indicators (KPI) of network slices. Then, we try to detect the malicious FL nodes, using DR and several XAI models, such as RuleFit, in order to enhance the level of trustiness, transparency, and the explanation of the FL‐based attacks detection, while adhering the data privacy, to different B5G network stakeholders.