Revenue-Model Learning for a Slice Broker by Considering Security and Dependability

Multi-access Edge Computing (MEC) and network slicing technologies enable the Fifth Generation (5 G) of cellular networks to provide new revolutionary network services. In 5G-MEC systems, service providers are at the forefront of delivering cutting-edge, low-latency services that redefine connectivity experiences. Service providers face significant challenges in monetizing their offerings while ensuring the security and dependability of their network slices. The slice broker plays a crucial role in the supply chain, enabling efficient resource allocation and dynamic service provisioning to meet diverse user demands. There is a trade-off between revenue generation and resource allocation efficiency, which should be investigated to identify optimal revenue strategies for the slice broker. Security and dependability are paramount in 5G-MEC systems due to the extensive sharing of resources and critical data processing at the edge. From a security perspective, malicious attackers can be active to inject attacks, e.g., backdoor attacks, data poisoning etc. From a dependability point of view, there can be faults that may mislead the system, e.g., Byzantine faults. We propose an adaptive method by which agents can detect malicious attackers and faults during revenue model learning, where both security and dependability are considered.