MANDO: Multi-Level Heterogeneous Graph Embeddings for Fine-Grained Detection of Smart Contract Vulnerabilities

Nguyen, Hoang Huy; Nguyen, Nhat-Minh; Xie, Chunyao; Ahmadi, Zahra; Kudendo, Daniel; Doan, Thanh-Nam; Jiang, Lingxiao

doi:10.1109/dsaa54385.2022.10032337

Cited by 13 publications

(1 citation statement)

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“…They first extracted features to construct a Heterogeneous Information Network (HIN) of smart contracts, then fed the relation matrices obtained from learned meta-paths in the transformation network into a convolutional network, and finally utilized node embeddings for classification tasks. Nguyen et al [121] proposed a novel heterogeneous graph representation approach called MANDO for learning the structure of heterogeneous contract graphs. MANDO developed a multiplex-path heterogeneous graph attention network to learn multi-layer embeddings of different types of nodes and their multiplex paths within the heterogeneous contract graph.…”

Section: Machine-learning-based Tools For Smart-contract Vulnerabilit...mentioning

confidence: 99%

Enhancing Smart-Contract Security through Machine Learning: A Survey of Approaches and Techniques

et al. 2023

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As blockchain technology continues to advance, smart contracts, a core component, have increasingly garnered widespread attention. Nevertheless, security concerns associated with smart contracts have become more prominent. Although machine-learning techniques have demonstrated potential in the field of smart-contract security detection, there is still a lack of comprehensive review studies. To address this research gap, this paper innovatively presents a comprehensive investigation of smart-contract vulnerability detection based on machine learning. First, we elucidate common types of smart-contract vulnerabilities and the background of formalized vulnerability detection tools. Subsequently, we conduct an in-depth study and analysis of machine-learning techniques. Next, we collect, screen, and comparatively analyze existing machine-learning-based smart-contract vulnerability detection tools. Finally, we summarize the findings and offer feasible insights into this domain.

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Section: Machine-learning-based Tools For Smart-contract Vulnerabilit...mentioning

confidence: 99%