Topology identification lays out the essential foundation for the operation monitoring and management of distribution networks. In this article, a novel two-stage data-driven topology identification approach is proposed for unbalanced three-phase distribution networks utilizing the measurements of smart meters. In the first stage, the phase sequence of each bus is recovered sequentially using the proposed phase identification method, where the similarity criteria are employed to reduce the influence of line impedance on voltage correlation. In the second stage, based on the phase identification results, the buses with relatively low active power injections are grouped into different clusters. By regarding each cluster as an aggregated node, the simplified system topology and the local topology of each cluster (i.e., each aggregated node) are identified sequentially using the ridge regression method. The full-scale topology of system is obtained by integrating the simplified system topology and the local topology of each cluster. Moreover, to further improve the identification efficiency, a novel optimal input design approach is proposed to select rich-information data from historical records. Various case studies are conducted to demonstrate the effectiveness and advantages of the proposed topology identification approach.

Smart contracts are integral to blockchain's growth, but their vulnerabilities pose a significant threat. Traditional vulnerability detection methods rely heavily on expert-defined complex rules that are labor-intensive and dificult to adapt to the explosive expansion of smart contracts. Some recent studies of neural network-based vulnerability detection also have room for improvement. Therefore, we propose SCVHunter, an extensible framework for smart contract vulnerability detection. Specifically, SCVHunter designs a heterogeneous semantic graph construction phase based on intermediate representations and a vulnerability detection phase based on a heterogeneous graph attention network for smart contracts. In particular, SCVHunter allows users to freely point out more important nodes in the graph, leveraging expert knowledge in a simpler way to aid the automatic capture of more information related to vulnerabilities. We tested SCVHunter on reentrancy, block info dependency, nested call, and transaction state dependency vulnerabilities. Results show remarkable performance, with accuracies of 93.72%, 91.07%, 85.41%, and 87.37% for these vulnerabilities, surpassing previous methods.

Blockchains, with intricate architectures, encompass various components, e.g., consensus network, smart contracts, decentralized applications, and auxiliary services. While offering numerous advantages, these components expose various attack surfaces, leading to severe threats to blockchains. In this study, we unveil a novel attack surface, i.e., the state storage, in blockchains. The state storage, based on the Merkle Patricia Trie, plays a crucial role in maintaining blockchain state. Besides, we design Nurgle, the first Denial-of-Service attack targeting the state storage. By proliferating intermediate nodes within the state storage, Nurgle forces blockchains to expend additional resources on state maintenance and verification, impairing their performance. We conduct a comprehensive and systematic evaluation of Nurgle, including the factors affecting it, its impact on blockchains, its financial cost, and practically demonstrating the resulting damage to blockchains. The implications of Nurgle extend beyond the performance degradation of blockchains, potentially reducing trust in them and the value of their cryptocurrencies. Additionally, we further discuss three feasible mitigations against Nurgle. At the time of writing, the vulnerability exploited by Nurgle has been confirmed by six mainstream blockchains, and we received thousands of USD bounty from them.