On Protection of the Next-Generation Mobile Networks Against Adversarial Examples

Abstract

As artificial intelligence (AI) has become an integral part of modern mobile networks, there is an increasing concern about vulnerabilities of intelligent machine learning (ML)-driven network components to adversarial effects. Due to the shared nature of wireless mediums, these components may be susceptible to sophisticated attacks that can manipulate the training and inference processes of the AI/ML models over the air. In our research, we focus on adversarial example attacks. During such an attack, an adversary aims to supply intelligently crafted input features to the target model so that it outputs a certain wrong result. This type of attack is the most realistic threat to the AI/ML models deployed in a 5G network since it takes place in the inference stage and therefore does not require having access to either the target model or the datasets during the training. In this study, we first provide experimental results for multiple use cases in order to demonstrate that such an attack approach can be carried out against various AI/ML-driven frameworks which might be present in the mobile network. After that, we discuss the defence mechanisms service providers may employ in order to protect the target network from adversarial effects.