Multi-vector Distributed Denial of Service (DDoS) attacks present a growing challenge to cybersecurity operations by combining various attack techniques across multiple network layers, often overwhelming both infrastructure and human decision-making. This study models the mental and technical response processes of a cybersecurity operator during such attacks using adaptive network modeling. Two models were developed: a base model simulating operator behavior without AI support, and an enhanced model featuring an adaptive AI-coach designed to assist in belief formation and attack mitigation. Both models integrate mental, physiological, and technical states and simulate responses over multiple attack cycles. Simulation results reveal that while the base model shows initial learning capacity, operator fatigue leads to performance decline and early mitigation collapse. In contrast, the AI-coach model maintains learning levels during later stages in the simulations through adaptive support, enabling prolonged mitigation effectiveness.