A trust based conceptual model for mitigating botnet driven DDoS in IoT

The rapid growth of Internet of Things (IoT) deployments has increased the exposure of resource-constrained devices to botnet-driven distributed denial-of-service (DDoS) attacks. Conventional detection and mitigation mechanisms may be insufficient in heterogeneous and dynamic IoT environments because they often rely on static rules, isolated observations, or binary trust decisions. This paper proposes SHoM, a conceptual trust-based framework for supporting the mitigation of botnet-driven DDoS attacks in IoT environments. SHoM combines multiple behavioral indicators, temporal trust updating, risk-aware adjustment, hierarchical trust aggregation, and graduated response actions. The mathematical formulation describes the logical relationships among node behavior, trust, risk, and mitigation decisions. The framework is designed to distinguish between normal, suspicious, and clearly untrusted behavior while reducing the likelihood of premature punitive actions against legitimate devices. The present study provides a theoretical formulation and qualitative analysis of the expected behavior of the framework. It does not report a real network simulation, an implemented prototype, benchmark-based evaluation, or experiments using real-world IoT traffic. Therefore, the contribution of this work is a conceptual foundation for future implementation and empirical validation. Future studies should evaluate SHoM in reproducible simulation and experimental environments using diverse IoT traffic patterns and adaptive botnet-driven DDoS scenarios.