Examining Anticipated Massive MIMO 5G Network Attacks Through Adversarial Examples

As we explore further into this relm of 5G technology, the importance of Massive Multiple-Input Multiple-Output (MIMO) systems stands out as the pivotal force propelling the advancement of mobile networks. The surging demand for data and the exponential proliferation of connected devices have ushered in a new era marked by the escalating complexity and cost of managing these extensive MIMO systems. One promising avenue of research addresses this complexity by integrating Artificial Intelligence (AI) models designed to gracefully accommodate the increasing demands of these systems. However, this fusion of AI technologies also introduces a host of novel security concerns and threats, most notably adversarial attacks. These malicious tactics exploit inherent vulnerabilities within AI models, casting a shadow of uncertainty over the performance and integrity of large-scale MIMO 5G and beyond networks. The integration of AI and its attendant challenges within the context of 5G networks remains conceptual, with early-stage implementation efforts underway. In this paper, we aim to offer a comprehensive analysis of the current landscape, shedding light on emerging trends and potential future threats, explicitly focusing on adversarial example-based attacks that target AI models within expansive MIMO 5G networks.