Eth2Vec: Learning Contract-Wide Code Representations for Vulnerability Detection on Ethereum Smart Contracts

Ashizawa, Nami; Yanai, Naoto; Cruz, Jason Paul; Okamura, Shingo

doi:10.1145/3457337.3457841

Cited by 76 publications

(24 citation statements)

References 48 publications

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“…Firstly, a comparison was made with tools such as Oyenete [25], Mythril [26], Smartcheck [27], and Securify [28] for reentrancy and timestamp dependency vulnerabilities. Secondly, we compared it with deep learning-based vulnerability detection methods, namely, Eth2Vec [33], DR-GCN, TMP, GCE, AME [35], AFS [36], and DeeSCV [38]. These methods are described in detail in Section 2.…”

Section: Resultsmentioning

confidence: 99%

“…Ashizawa et al proposed Eth2Vec, a machine learningbased static analysis tool for smart contract vulnerability detection. It automatically learns the features of vulnerable smart contract bytecodes through a neural network for natural language processing [33]. Eth2Vec detects vulnerabilities with a high degree of accuracy by implicitly extracting features and combining lexical semantics between contracts, even after rewriting the code.…”

Section: Existing Methods For Detecting Smart Contract Vulnerabilitiesmentioning

confidence: 99%

“…Firstly, a comparison was made with existing automated security auditing tools, namely, Oyenete [25], Mythril [26], Smartcheck [27], and Securify [28]. Secondly, we compared it with deep learning-based vulnerability detection methods, namely, DeeSCV [38], Eth2Vec [33], DR-GCN, TMP, GCE, AME [35],…”

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confidence: 99%

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SPCBIG-EC: A Robust Serial Hybrid Model for Smart Contract Vulnerability Detection

Zhang

Jin³

et al. 2022

Sensors

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With countless devices connected to the Internet of Things, trust mechanisms are especially important. IoT devices are more deeply embedded in the privacy of people’s lives, and their security issues cannot be ignored. Smart contracts backed by blockchain technology have the potential to solve these problems. Therefore, the security of smart contracts cannot be ignored. We propose a flexible and systematic hybrid model, which we call the Serial-Parallel Convolutional Bidirectional Gated Recurrent Network Model incorporating Ensemble Classifiers (SPCBIG-EC). The model showed excellent performance benefits in smart contract vulnerability detection. In addition, we propose a serial-parallel convolution (SPCNN) suitable for our hybrid model. It can extract features from the input sequence for multivariate combinations while retaining temporal structure and location information. The Ensemble Classifier is used in the classification phase of the model to enhance its robustness. In addition, we focused on six typical smart contract vulnerabilities and constructed two datasets, CESC and UCESC, for multi-task vulnerability detection in our experiments. Numerous experiments showed that SPCBIG-EC is better than most existing methods. It is worth mentioning that SPCBIG-EC can achieve F1-scores of 96.74%, 91.62%, and 95.00% for reentrancy, timestamp dependency, and infinite loop vulnerability detection.

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Section: Resultsmentioning

confidence: 99%

Section: Existing Methods For Detecting Smart Contract Vulnerabilitiesmentioning

confidence: 99%

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SPCBIG-EC: A Robust Serial Hybrid Model for Smart Contract Vulnerability Detection

Zhang

Jin³

et al. 2022

Sensors

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“…Eth2Vec [196], as an ML-based static analysis tool, owns the robustness against code rewriting of the smart contracts. This tool can automatically learn features and separate feature extraction from the technical difficulty of the smart contracts analysis by ML.…”

Section: Intellectuality Smart Contract Tailoring To Eimentioning

confidence: 99%

Integrating Edge Intelligence and Blockchain: What, Why, and How

Wang

Ren

Qiu

et al. 2022

IEEE Commun. Surv. Tutorials

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Driven by an unprecedented boom in artificial intelligence (AI) and Internet of Things (IoT), edge intelligence (EI) pushes the frontier of AI from cloud to network edge, serving as a remarkable solution that unlocks the full potential of AI services. It is yet facing critical challenges in its decentralized management and security, limiting its capabilities to support services with numerous requirements. In this context, blockchain (BC) has been seen as a promising solution to tackle the above issues, and further support EI. Based on the number of citations or the relevance of emerging methods, this paper presents the results of a literature survey on the integration of EI and BC. Accordingly, we summarize the recent research efforts reported in the existing works on EI and BC. We then paint a comprehensive picture of the limitations of EI and why BC could benefit from EI. From there, we explore how BC benefits EI in terms of computing power management, data administration, and model optimization. In order to narrow the gap between immature BC and EI-amicable BC, we also probe into how to tailor BC to EI from four perspectives, including flexible consensus protocol, effective incentive, intellectuality smart contract, and scalability. Finally, some research challenges and future directions are presented. Different from existing surveys, our work focuses on the integration of EI and BC, develops some general models to help the reader build relevant optimization models in the integrated system, as well as provides detailed tutorials on implementation. We anticipate that this survey will motivate further discussions on the synergy of EI and BC, and offer some guidance in EI, BC, future networks, and other areas.Index Terms-Edge intelligence (EI), blockchain (BC), edge computing, decentralization, distributed ledger technology (DLT).

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“…Ashizawa et al [57] presented Eth2Vec-a deep learning vulnerability detection system for Ethereum smart contracts. To evaluate their proposed system, they compared it to SVM.…”

Section: Cryptojacking Malware and Securitymentioning

confidence: 99%

Synergy of Blockchain Technology and Data Mining Techniques for Anomaly Detection

2021

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Blockchain and Data Mining are not simply buzzwords, but rather concepts that are playing an important role in the modern Information Technology (IT) revolution. Blockchain has recently been popularized by the rise of cryptocurrencies, while data mining has already been present in IT for many decades. Data stored in a blockchain can also be considered to be big data, whereas data mining methods can be applied to extract knowledge hidden in the blockchain. In a nutshell, this paper presents the interplay of these two research areas. In this paper, we surveyed approaches for the data mining of blockchain data, yet show several real-world applications. Special attention was paid to anomaly detection and fraud detection, which were identified as the most prolific applications of applying data mining methods on blockchain data. The paper concludes with challenges for future investigations of this research area.

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Eth2Vec: Learning Contract-Wide Code Representations for Vulnerability Detection on Ethereum Smart Contracts

Cited by 76 publications

References 48 publications

SPCBIG-EC: A Robust Serial Hybrid Model for Smart Contract Vulnerability Detection

SPCBIG-EC: A Robust Serial Hybrid Model for Smart Contract Vulnerability Detection

Integrating Edge Intelligence and Blockchain: What, Why, and How

Synergy of Blockchain Technology and Data Mining Techniques for Anomaly Detection

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