Innovative Cloud-Based E-Commerce Fraud Prevention Using GAN-FS, Fuzzy-Rough Clustering, Smart Contracts, and Game-Theoretic Models

The rise in online transactions will lead to increased fraud, often flagging legitimate users. Cloud systems provide extensive monitoring of large networks. By game theory, group analysis, GAN-FS synthetic generation, and leveraging blockchain verification, an adaptive intelligence protection system is created to respond to evolving threats. GAN-FS generates artificial bidding schemes as fuzzy-rough clustering detects subtle patterns in conflicting data. Game theory strengthens safeguards, while blockchain's self-enforcing contracts automate security, ensuring reliable cloud coverage and surveillance. This system enhances scalability, volumes, and real-time monitoring. Smart contracts secure transactions, fuzzy-rough clustering improves pattern recognition, and GANs with synthetic data improve fraud detection. It predicts fraudulent strategies using climate-scalable cloud architecture, secure, offering flexible, and game-theoretical simulators. In addition to automating transactions, synthetic data generation drives fraud detection. The proposed method achieved 91.8% scalability, 93.7% recall of real cases, and 94.3% classification accuracy. By combining game theory, fuzzy-rough clustering, GAN-FS, and blockchain smart contracts, false positives are reduced and real-time flexibility is enhanced. Hybrid frameworks are therefore superior to conventional fraud detection techniques, with a flexible, scalable, and robust means of protecting online transactions and addressing sophisticated fraud techniques.