An Iterative quantum chaotic security method design for big data deployments using a multi-model framework for key generation, threat modelling, and anomaly-resilient validation.

The forecast of big data is becoming exponential in major sectors, which further points toward a necessity for strong, scalable, and intelligent security frameworks. The existing mechanisms are failing to handle high-volume, high-velocity settings due to static cryptographic structures, limited entropy, and inadequate adaptability towards developing threats. On the other hand, the most widely used traditional models are often threat-class dependent, thereby lacking real-time resilience assessment coupled with dynamic key management. This paper puts forth a proposal of a novel multi-layered big data security framework partly driven by QSSS and Adaptive Intelligence. It integrates advanced entropy-based key generation, dynamic threat modeling, hybrid blockchain integrity, adaptive encryption-compression, and intelligent intrusion detection. All these layers ensure end-to-end confidentiality, integrity, and anomaly resilience. Empirical evaluations show that detection has improved significantly, with intrusion detection exceeding 95 % purity, while tampering detection reached over 96 %, and storage optimization reached 60 %. The proposed framework embodies a next-generation solution for securing adaptable and scalable big-data infrastructures in high-supportability environments, where changing threats call for continuing adaptation. Multi-layered architecture combining chaos-driven entropy and adaptive intelligence. Integrated protection ensuring confidentiality, integrity, and resilience. Scalable design tailored for dynamic, high-growth data ecosystems.