Joint Trust-Aware Topology Adaptation and Resource-Constrained Patching for Malware Containment in the Social Internet of Things

Social Internet of Things (SIoT) relationships enable service discovery and create pathways for malware propagation. We develop a heterogeneous susceptible–exposed–infected–recovered model coupling resource-constrained patching with reversible, trust-aware relationship suppression. A controlled next-generation operator yields a reproduction threshold, local stability, spectral monotonicity, and a uniform eradication condition. A finite-horizon security–service objective and its necessary conditions motivate joint adaptive topology and patching (JATP), a feasible sampled-data feedback heuristic combining prevalence, structural influence, trust evidence, and service importance. Under reference capacities, JATP reduces peak infection and cumulative burden by 49.43% and 38.97% relative to risk-only patching while retaining 0.8184 of weighted relationship intensity. Under a common expenditure ceiling, its objective is 59.56% above the best feasible offline nonlinear-programming value found. Explicit-network tests support aggregation under moderate heterogeneity and identify larger prediction errors under strong degree variation and susceptible-entry churn. Trust-related gains depend on alignment with model-defined propagation risk; field calibration remains open. These results characterize the operating scope of a feedback policy that coordinates two defense channels under hard resource limits. The framework links epidemic thresholds, intervention allocation, and service preservation to support the design of resource-limited SIoT containment.