This paper developed an artificial simulation of the Distributed Trust and Cybersecurity Intelligence-Driven Network Slice Lifecycle Management (DT-CID-N-SLM) architecture to assess how well it would perform under different attack types and different levels of demand for services. It evaluated the DT-CID-N-SLM under a variety of service demand and threat conditions using this artificial simulation environment. The results included very accurate trust assessments, higher rates of detecting threats, better availability of slices, greater overall resilience than competing solutions, as well as several enhanced visualizations and analysis at the signal level of network communications. The visualizations on constellation diagrams, spectral density, and eye diagrams all indicated improvements in the quality of signals being transmitted across the network after applying the optimization techniques. However, the results did show some degree of fluctuation in heavily congested conditions; however, these were still within the acceptable operating range.
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Agarwal et al. (2026) studied this question.
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