Design of an Integrated Model for Multi-Stage Quantum-Temporal Prevention and Hyperdimensional Forecasting of IoT Botnet Attacks

Abstract

Botnet assaults silently spread through low-power sensors, consumer routers, and factory controls due to the rapid rise of unmanaged IoT devices. Prior research on traffic classification or static signature matching fails when adversaries randomize packet timing or imitate benign traffic patterns. Since mitigation methods are often adopted later, networks have slow containment cycles and minimal visibility into device cluster infection pressure. Recent botnet breakouts may still strain bandwidth and hamper edge resources in well-monitored systems due to these limitations. This study describes a five-step chained process to detect and stop IoT botnets before large-scale coordination. We begin with Adaptive Quantum-Temporal Traffic Embedding (AQTTE), which preserves uncertain temporal behaviors in 256-dimensional embeddings to differentiate ambiguous flows. Federated Trust-Gradient Graph Neural Refinement (FTG-GNR) uses these embeddings to assign dynamic trust weights across device-to-device connections when firmware peculiarities cause inconsistent behavior. The 10,000-dimensional state vectors of the Hyperdimensional Botnet Propagation Forecasting System (HBPFS) estimate multi-hop spread under noisy traffic to anticipate infection zones. The Neuro-Adversarial Defense Synthesis Engine (NADSE) uses that forecast to create adversarial simulation defense plans to decrease confinement windows. Finally, the Socio-Topological Attack Surface Resilience Analyzer (ST-ASRA) evaluates residual infection potential across connection and ownership layers, which may identify cross Vendor Vulnerabilities In Process. Cluster purity, outbreak isolation, forecast horizons, and attack surface contraction improve for the process. These findings show security should learn, foresee, and adapt rather than react after damages.