  Semantic communication, emerging as a breakthrough beyond the classical
Shannon paradigm, aims to convey the essential meaning of source data rather
than merely focusing on precise yet content-agnostic bit transmission. By
interconnecting diverse intelligent agents (e.g., autonomous vehicles and VR
devices) via semantic communications, the semantic communication networks
(SemComNet) supports semantic-oriented transmission, efficient spectrum
utilization, and flexible networking among collaborative agents. Consequently,
SemComNet stands out for enabling ever-increasing intelligent applications,
such as autonomous driving and Metaverse. However, being built on a variety of
cutting-edge technologies including AI and knowledge graphs, SemComNet
introduces diverse brand-new and unexpected threats, which pose obstacles to
its widespread development. Besides, due to the intrinsic characteristics of
SemComNet in terms of heterogeneous components, autonomous intelligence, and
large-scale structure, a series of critical challenges emerge in securing
SemComNet. In this paper, we provide a comprehensive and up-to-date survey of
SemComNet from its fundamentals, security, and privacy aspects. Specifically,
we first introduce a novel three-layer architecture of SemComNet for
multi-agent interaction, which comprises the control layer, semantic
transmission layer, and cognitive sensing layer. Then, we discuss its working
modes and enabling technologies. Afterward, based on the layered architecture
of SemComNet, we outline a taxonomy of security and privacy threats, while
discussing state-of-the-art defense approaches. Finally, we present future
research directions, clarifying the path toward building intelligent, robust,
and green SemComNet. To our knowledge, this survey is the first to
comprehensively cover the fundamentals of SemComNet, alongside a detailed
analysis of its security and privacy issues.
