Design and performance comparison of two implemented trust evaluation models for fifth generation (5G) and beyond 5G (B5G) network slicing using an interval type-2 fuzzy logic system

Trust evaluation for fifth generation (5G) and beyond 5G (B5G) network slicing has become increasingly critical due to the complexity of multi-tenant orchestration, dynamic resource allocation, and strict quality of service (QoS) requirements. However, conventional trust evaluation methods cannot capture the uncertainty coming from measurement noise, linguistic ambiguity, and dynamic network conditions. In this paper, we propose a fuzzy-based system for slice trust evaluation (FSSTE) designed for 5G/B5G network slicing environments. We implement two models: FSSTEM1 and FSSTEM2. FSSTEM1 considers three input parameters: QoS, security posture (SP), and isolation integrity (II), while FSSTEM2 extends this framework by incorporating resource reliability (RR) as a fourth parameter. Both models use interval type-2 fuzzy logic system to decide the slice trust (ST) output value. We evaluated the implemented models by simulations and found that when QoS, SP, II, and RR values increase, the ST value increases consistently. For FSSTEM1, when all parameters reach 0.9, the ST value is 0.868, which is suitable for mission-critical applications. For FSSTEM2, the RR provides 21% trust improvement under poor QoS conditions, which is suitable for safety-critical deployments. Parameter sensitivity analysis for FSSTEM1 shows that QoS and II have the same impact on ST (0.251). While SP has a stronger impact (0.257) compared with QoS and II. For FSSTEM2, the higher maximum footprint uncertainty of FSSTEM2 (0.249 vs. 0.190) suggests that four-parameters assessment involves greater uncertainty. FSSTEM2 is more complex than FSSTEM1 but provides better trust evaluation, especially in dynamic multi-tenant environments where RR changes.