MANDO-HGT: Heterogeneous Graph Transformers for Smart Contract Vulnerability Detection

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

doi:10.1109/msr59073.2023.00052

Cited by 3 publications

(5 citation statements)

References 56 publications

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“…The MANDO-HGT framework [65] adapts heterogeneous graph transformers (HGTs) with customized meta relations for graph nodes and edges to learn their embeddings and train classifiers for detecting various vulnerabilities. In contrast, the FBB-VD scheme [66] uses a graph neural network to combine multiple code representations and detect vulnerabilities in SCs.…”

Section: Deep Learning Modelsmentioning

confidence: 99%

“…The present machine learning-based vulnerability detection techniques mainly focus on known vulnerabilities, while unknown vulnerabilities in SCs have been rarely investigated. This subsection delves into examining recently published articles [40,52,65,71] that have tackled this particular issue.…”

Section: Unknown Vulnerabilitiesmentioning

confidence: 99%

“…The methods proposed by Zhang et al [52], Nguyen et al [65], and Zou et al [71] utilize deep learning models to detect unknown vulnerabilities in SCs. MODNN [52] uses a CNN-based model to identify more unknown vulnerabilities without needing a specialist or predefined knowledge.…”

Section: Unknown Vulnerabilitiesmentioning

confidence: 99%

“…This method learns vulnerabilities in unknown patterns from explicit features. By contrast, Nguyen et al [65] and Zou et al [71] use a GNN-driven strategy to deal with unknown vulnerabilities. The framework proposed by Nguyen et al [65] can be re-trained for new types of vulnerabilities and detect vulnerabilities in large sets of contracts efficiently and accurately.…”

Section: Unknown Vulnerabilitiesmentioning

confidence: 99%

“…By contrast, Nguyen et al [65] and Zou et al [71] use a GNN-driven strategy to deal with unknown vulnerabilities. The framework proposed by Nguyen et al [65] can be re-trained for new types of vulnerabilities and detect vulnerabilities in large sets of contracts efficiently and accurately. In contrast, the SAGP approach [71] is equipped with the self-attention mechanism to detect vulnerabilities that may not be identifiable by expert knowledge, thereby minimizing the possibility of errors that can arise from manually established detection patterns.…”

Section: Unknown Vulnerabilitiesmentioning

confidence: 99%

See 4 more Smart Citations

Ethereum Smart Contract Vulnerability Detection and Machine Learning-Driven Solutions: A Systematic Literature Review

Kiani,

Sheng

2024

Electronics

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In recent years, emerging trends like smart contracts (SCs) and blockchain have promised to bolster data security. However, SCs deployed on Ethereum are vulnerable to malicious attacks. Adopting machine learning methods is proving to be a satisfactory alternative to conventional vulnerability detection techniques. Nevertheless, most current machine learning techniques depend on sufficient expert knowledge and solely focus on addressing well-known vulnerabilities. This paper puts forward a systematic literature review (SLR) of existing machine learning-based frameworks to address the problem of vulnerability detection. This SLR follows the PRISMA statement, involving a detailed review of 55 papers. In this context, we classify recently published algorithms under three different machine learning perspectives. We explore state-of-the-art machine learning-driven solutions that deal with the class imbalance issue and unknown vulnerabilities. We believe that algorithmic-level approaches have the potential to provide a clear edge over data-level methods in addressing the class imbalance issue. By emphasizing the importance of the positive class and correcting the bias towards the negative class, these approaches offer a unique advantage. This unique feature can improve the efficiency of machine learning-based solutions in identifying various vulnerabilities in SCs. We argue that the detection of unknown vulnerabilities suffers from the absence of a unique definition. Moreover, current frameworks for detecting unknown vulnerabilities are structured to tackle vulnerabilities that exist objectively.

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Section: Deep Learning Modelsmentioning

confidence: 99%

Section: Unknown Vulnerabilitiesmentioning

confidence: 99%