AI, intellectual security and demographic challenges in the context of regional integration

Abstract

Ethereum smart contracts enable decentralized applications, yet they are highly prone to vulnerabilities, which results in severe financial losses. Objective: This research develops an intelligent and automated detection framework called Osprey-Hen Optimized Gated Graph Sequence with Vulnerability Aware Graph Attention  (Oshen-VAGA-GGS) Network for detecting vulnerabilities in Ethereum smart contracts. Method: The acquired data are preprocessed via normalizing Solidity, stripping comments, flattening inheritance. Moreover, a heterogeneous multigraph is constructed by combining Abstract Syntax Tree (AST), basic-block Control Flow Graph (CFG), def-use Data Flow Graph (DFG), and inter-contract call graph using Slither, and labelled edges as control/data/call. Then significant features are extracted via Word2Vec-embed opcodes and identifiers, numeric literal bins, positional encodings and one-hot node-types and balance the data by SMOTE during training only. These features are passed to the proposed Gated Graph Sequence Neural Network (GGNN) and Set2Set readout with attention pooling is added to give contract-level and function-level logits. To enhance the efficacy of the proposed Oshen-VAGA-GGS, an optimization is developed using wrap AdamW with OsHen metaheuristic global osprey exploration, local hen exploitation. Novelty: The VAGA enhances heterogeneous graph representations by explicitly modeling vulnerability semantics. The framework represents smart contracts using structural and semantic program information and employs gated graph propagation to learn long-range dependencies among contract components. Findings: Comprehensive experiments are conducted against GNN, Bi-LSTM, MLP+LSTM, and BERT-ATT-BiLSTM baselines. With 80% training data, the corresponding results increase to 98.57%, 98.56%, 99.79%, 98.57%, and 98.56%, respectively. In the ablation analysis, the complete GGNN + OsHen-VAGA configuration achieves 98.80% accuracy and 98.79% F1-score, compared with 94.99% for the baseline GGNN.