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This Deliverable presents the current security landscape of 5G networks, as well as the evolution of requirements and trends in 5G security. It includes a summary of the 5G threat landscape, the 5G networks classification criteria and their threat taxonomy; a description of security requirements of 5G systems, divided into domain-specific use cases, and the elicitation of security requirements from relevant stakeholders in 5G; the current status of 5G networks, the solutions state for securing 5G systems, the standardization effort in the domain of 5G security, the relevant 5G projects, and open source initiatives; and a description of future trends and technologies in 5G networks, their limitations, and gaps related to the security of 5G networks. This deliverable aims to provide a basis for the identification of use cases and the development of 5G security enablers in INSPIRE-5Gplus.

Funding Information: This work was supported in part by the European Union's Horizon 2020 research and innovation programme under the MonB5G project (Grant No. 871780); and the Academy of Finland Project 6Genesis Flagship (Grant No. 318927). Publisher Copyright: © 2021 | openaire: EC/H2020/871780/EU//MonB5G Along with the high demand for network connectivity from both end-users and service providers, networks have become highly complex; and so has become their lifecycle management. Recent advances in automation, data analysis, artificial intelligence, distributed ledger technologies (e.g., Blockchain), and data plane programming techniques have sparked the hope of the researchers’ community in exploring and leveraging these techniques towards realizing the much-needed vision of trustworthy self-driving networks (SelfDNs). In this vein, this article proposes a novel framework to empower fully distributed trustworthy SelfDNs across multiple domains. The framework vision is achieved by exploiting (i) the capabilities of programmable data planes to enable real-time in-network telemetry collection; (ii) the potential of P4 – as an important example of data plane programming languages – and AI to (re)write the source code of network components in a fashion that the network becomes capable of automatically translating a policy intent into executable actions that can be enforced on the network components; and (iii) the potential of blockchain and federated learning to enable decentralized, secure and trustable knowledge sharing between domains. A relevant use case is introduced and discussed to demonstrate the feasibility of the intended vision. Encouraging results are obtained and discussed.