Design of an Iterative Method with Unified Privacy-Preserving Authentication and Intelligent Forensic Framework for Cloud and IoT Security Analysis

Abstract

Secure authentication along with malware detection are very important steps in modern cloud or IoT environment, with, privacy, accountability, and resilience against advanced threats. The present day anonymous authentication protocols reportedly have a high cryptographic overhead, low traceability, or static privacy mechanisms, while the current IoT malware forensic approaches happen to suffer from gradient leakage, low adaptability to zero day attacks, and slow resilience. This paper presents a comprehensive multi model framework combining five novel methods. The Dual Ledger Accountability Embedded Authentication (DLAA) model combines a primary blockchain with a secondary lightweight audit ledger and zero knowledge proofs, enabling revocable accountability without identity disclosure. The Layered Privacy Gradient Synthesis (LPGS) network applies adaptive differential privacy through learned gradient perturbations, balancing anonymity with service utility. The Quantum Inspired Entropy Guided Authentication Matrix (QEAM) replaces the key exchange with entropy driven, quantum inspired encoding, enabling faster keyless authentication. For IoT forensics, the Federated Swarm Vector Autoencoder Forensics (FSVAF) framework uses swarm optimized federated learning to detect anomalies in compressed latent space, reducing gradient leakage and improving zero day detection possibilities. The Temporal Hybrid Graph Reasoning Engine (THGRE) fuses symbolic rules with neural inference over evolving knowledge graphs for quick malware traceback. The experimental output reveals that the authentication time is reduced by 38%, with 94% malware detection accuracy in adaptive attack conditions, and is able to resolve forensics up to 67% more rapidly than previous static approaches with significantly reduced overhead. This framework collectively enhance privacy, accountability, scalability, and forensic dependability, making it efficient solution for next generation cloud and IoT ecosystems.