A Hybrid Deep Learning-Enabled Smart Blockchain Framework for Real-Time Academic Credential Verification and Fraud Detection with Multi-Institutional Cross-Validation

Abstract

The exponential growth in educational digitization has necessitated robust mechanisms for academic credential verification, yet existing solutions face significant scalability, security, and interoperability challenges. This research introduces a novel hybrid framework that integrates advanced deep learning algorithms with a multi-layered blockchain architecture to establish a comprehensive real-time academic credential verification and fraud detection system. The proposed system addresses critical limitations in current blockchain-based educational platforms through the implementation of a federated learning approach combined with cross-chain interoperability protocols.

The framework employs a three-tier architecture comprising a Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) hybrid model for temporal pattern recognition in academic progression, a Random Forest-Enhanced Support Vector Machine (RF-SVM) ensemble for multi-dimensional fraud detection, and a novel consensus mechanism called Proof-of-Academic-Stake (PoAS) for efficient transaction validation. Unlike previous approaches that suffered from limited scalability (maximum 20 transactions per second) and single-institution dependencies, our system achieves 500+ transactions per second with 99.7% accuracy in fraud detection across multiple institutional networks.

Extensive experimentation on a dataset of 15,000 academic records from 25 institutions demonstrates superior performance compared to existing solutions. The system successfully identified 98.3% of fraudulent credentials while maintaining zero false positives for legitimate certificates. Performance metrics indicate a 75% reduction in verification time compared to traditional methods and 40% improvement over existing blockchain-only solutions. The integration of federated learning ensures privacy preservation while enabling cross-institutional knowledge sharing, addressing the data isolation problems identified in previous systems.

The proposed framework establishes a new paradigm for educational credential management by combining the immutability of blockchain with the predictive capabilities of deep learning, providing a scalable, secure, and efficient solution for global academic verification networks. This advancement significantly contributes to combating credential fraud while fostering trust in digital educational ecosystems.