Enhancing Security of Cross-Chain Bridge Smart Contracts Using Multi-Model Deep Learning Techniques for Vulnerability Detection

Abstract

One of the key elements facilitating cross-chain bridges is the ability to connect assets and data from different chains, irrespective of their incompatibility. They provide significant functionality with their multi-chain logic, but also add some complexity to the security problems, as several bridge smart contract hacks have caused major financial losses. This work explores a family of deep-learning architectures for proactive vulnerability detection of cross-chain bridge smart contracts, intentionally deviating from the recurrent and generic graph baselines. Five different models are analysed in this paper, namely a convolutional sequence model (TextCNN), attention-augmented recursive model (Attention-BiLSTM), inductive graph model (GraphSAGE), graph-attention network (GAT), and data-flowaware transformer (GraphCodeBERT). They are optimized and evaluated on the SmartAxe cross-chain bridge dataset to learn syntactic and semantic security patterns in each model. A systematic method is used, which includes preprocessing, representation learning, training and comparative evaluation. Experimental results show significantly higher precision and adaptability for models that rely on attention and/or graph structures, enabling more flexible and secure cross-chain interoperability.