In recent years, graph neural networks have demonstrated strong capabilities in processing graph-structured data and have made significant progress in the field of smart contract vulnerability detection. This paper introduces HF-Sec, a novel framework for smart contract vulnerability detection. The framework first automatically generates heterogeneous contract graphs from the source code of Ethereum smart contracts to represent the control flow and function call relationships of the code. Then, by using a multi-source attention mechanism, the framework is able to synthesize features from different sources to capture key information from multiple perspectives. In addition, HF-Sec utilizes Fast Graph Transformer Networks and Kolmogorov-Arnold Networks to automatically extract mission-critical meta-paths and enhance the interpretability of the model. We performed experimental validation on a mixed dataset containing 423 contracts with vulnerabilities and 2742 contracts without vulnerabilities. The experimental results show that HF-Sec can significantly improve the accuracy of smart contract vulnerability detection, which is better than the methods based on machine learning or traditional analysis techniques. Through a series of ablation experiments, we further verified the importance of various key components in HF-Sec to improve the detection accuracy.

With the development of blockchain applications, demand for cross-chain technology has been increasing. Relay chain mode is the state-of-the-art and mainstream solution nowadays. However, the relay chain mode suffers from poor performance, which stems from its core facility – the relay chain. Therefore, guiding its improvement and parameters configuration is vital. Currently, there is no specialized performance model of the relay chain. The cross-chain scenario involves receiving transactions from blockchains and uniformly verifying them, while general blockchain models are not applicable for it. Relay chains are characterized by the following features: transaction arrival in batches with uncertain sizes, updating block headers for simplified payment verification (SPV), Byzantine fault tolerance (BFT) type protocol, and different packaging rules. This work first proposes an analytical framework for relay chain performance. It captures the mentioned features by constructing a batch-arrival and bulk-service model. We give a concrete calculation of the relay chain with practical BFT (PBFT) consensus and develop a method to arrive at the computational forms of two essential performance descriptors: system throughput and cross-chain transaction confirmation delay. Through this model, we can judge accurately whether the relay chain is overloaded, and eliminate the overload state by tuning the parameters; and we can evaluate the system performance under different traffic and design parameters. Finally, we verify the model through experiments. With our study, operators can configure the system parameters effectively and improve the relay chain to meet the requirements of practical use.

In recent years, the frequent occurrence of phishing scams on Ethereum has posed serious threats to transaction security and the financial safety of users. This paper proposes an Ethereum phishing scam detection method based on Hyperbolic Neural Networks (HGNNs) and temporal information. The method maps the Ethereum transaction network to hyperbolic space for structural feature extraction, effectively capturing hierarchical structures and complex relationships within the graph, thereby improving the accuracy of phishing scam detection. The model includes a structural feature extraction module and a temporal feature extraction module. It uses HGNN and self-attention mechanism to extract the structural features of the transaction graph, and uses a multi-head attention mechanism to capture the dynamic evolution pattern of the graph. Experimental validation on real Ethereum datasets demonstrates that the proposed model outperforms benchmark models, showcasing its effectiveness.

Sharding is a prevalent approach for addressing performance issues in blockchain. To reduce governance complexities and ensure system security, a common practice involves a relay chain to coordinate cross-shard transactions. However, with a growing number of shards and cross-shard transactions, the single relay chain usually first suffers from performance bottleneck and shows poor scalability, thus making the relay chain's scalability vital for sharding systems. To solve this, we proposeMitosis, the first multi-relay architecture to improve the relay chain's scalability by sharding the relay chain itself. Our proposed relay sharding algorithm dynamically adjusts the number of relays or optimizes the topology between relays and shards to adaptively scale up relay chain's performance. Furthermore, to guarantee the security of the multi-relay architecture, a new validator reconfiguration scheme is designed, accompanied by a comprehensive security analysis ofMitosis. Through simulation experiments on two mainstream relay chain paradigms, we demonstrate thatMitosiscan achieve high scalability and outperform state-of-the-art baselines in terms of workload of relays, relay chain throughput, and transaction latency.