Blockchain-Enhanced IoT Framework for Secure Environmental Data Integrity

The rapid expansion of Internet of Things (IoT)-integrated cloud systems has heightened the need for secure and reliable environmental data management that ensures integrity, privacy, and confidentiality. However, these IoT-Cloud environments remain vulnerable to attacks, manipulation, and unauthorized access, compromising trust and decision-making. To address these challenges, this study proposes the Blockchain-based Graph Sample and Aggregate Cycle-Consistent Adversarial Adaptation Network with Puma Optimizer (GSACCA-Net-PO). The framework begins with blockchain-based authentication, followed by anomaly detection via a robust IDS. Data is preprocessed using a Simplified Adaptive Fuzzy Min–Max Neural Network, and feature extraction is achieved through a Circulatory System-based Duck Swarm Algorithm. GSACCA-Net classifies threats with loss optimization via the Puma Optimizer. Secured storage and access are managed through Lightweight Dynamic Elliptic Curve Cryptography. Experiments using BoT-IoT and TON_IoT datasets yield accuracies of 99.41% and 99.27%, high integrity (98%), and efficient encryption times, validating the framework’s effectiveness.