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

Zhang, Lejun; Li, Yuan; Jin, Tao; Wang, Weizheng; Jin, Zilong; Zhao, Chunhui; Cai, Zhennao; Chen, Huiling

doi:10.3390/s22124621

Cited by 27 publications

(12 citation statements)

References 62 publications

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“…In [24], Word2Vec was used to encode the smart contract fragments as the input to a sophisticated deep-learning model referred to as the Serial-Parallel Convolutional Bidirectional Gated Recurrent Network Model incorporating Ensemble Classifiers (SPCBIG-EC). Two types of smart contract vulnerability, re-entrancy and timestamp dependency, were detected separately.…”

Section: Related Workmentioning

confidence: 99%

The Impact of Input Types on Smart Contract Vulnerability Detection Performance Based on Deep Learning: A Preliminary Study

Aldyaflah,

Zhao,

Yang

et al. 2024

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Stemming vulnerabilities out of a smart contract prior to its deployment is essential to ensure the security of decentralized applications. As such, numerous tools and machine-learning-based methods have been proposed to help detect vulnerabilities in smart contracts. Furthermore, various ways of encoding the smart contracts for analysis have also been proposed. However, the impact of these input methods has not been systematically studied, which is the primary goal of this paper. In this preliminary study, we experimented with four common types of input, including Word2Vec, FastText, Bag-of-Words (BoW), and Term Frequency–Inverse Document Frequency (TF-IDF). To focus on the comparison of these input types, we used the same deep-learning model, i.e., convolutional neural networks, in all experiments. Using a public dataset, we compared the vulnerability detection performance of the four input types both in the binary classification scenarios and the multiclass classification scenario. Our findings show that TF-IDF is the best overall input type among the four. TF-IDF has excellent detection performance in all scenarios: (1) it has the best F1 score and accuracy in binary classifications for all vulnerability types except for the delegate vulnerability where TF-IDF comes in a close second, and (2) it comes in a very close second behind BoW (within 0.8%) in the multiclass classification.

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Section: Related Workmentioning

confidence: 99%

The Impact of Input Types on Smart Contract Vulnerability Detection Performance Based on Deep Learning: A Preliminary Study

Aldyaflah,

Zhao,

Yang

et al. 2024

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“…A number of publications have demonstrated how similar techniques could be imaginatively applied to new fields of study. In the area of Smart Contract Vulnerability Detection, Zhang et al [4] developed SPCBIG-EC, a robust serial hybrid model designed exclusively for discovering vulnerabilities in smart contracts. SPCBIG-EC was created with the intention of finding vulnerabilities in smart contracts.…”

Section: Related Workmentioning

confidence: 99%

Quantum Deep Neural Network Based Classification of Attack Vectors on the Ethereum Blockchain

Rajawat,

Goyal,

Kumar

et al. 2024

ICST Transactions on Scalable Information Systems

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INTRODUCTION: The implementation of robust security protocols is imperative in light of the exponential growth of blockchain-based platforms such as Ethereum. The importance of developing more effective strategies to detect and counter potential attacks is growing in tandem with the sophistication of the methods employed by attackers. In this study, we present a novel approach that leverages quantum computing to identify and predict attack vectors on the Ethereum blockchain. OBJECTIVES: The primary objective of this study is to suggest an innovative methodology for enhancing the security of Ethereum by leveraging quantum computing. The purpose of this study is to demonstrate that QRBM and QDN are efficient in identifying and predicting security flaws in blockchain transactions. METHODS: We combined methods from quantum computing with social network research approaches. An enormous dataset containing both genuine Ethereum transactions and a carefully chosen spectrum of malicious activity indicative of popular attack vectors was used to train our model, the QRBM. Thanks to the dataset, the QRBM was able to learn to distinguish between typical and out-of-the-ordinary activities. RESULTS: In comparison to more conventional deep learning models, the QRBM showed substantially better accuracy when it came to identifying transaction behaviours. The model's improved scalability and efficiency were made possible by its quantum nature, which is defined by features like entanglement and superposition. Specifically, the QRBM handled non-informative inputs better and solved problems faster. CONCLUSION: This study paves the way for further investigation into quantum-enhanced cybersecurity measures and highlights the promise of quantum neural networks in strengthening the security of blockchain technology. According to our research, quantum computing has the potential to be an essential tool in creating Ethereum-style blockchain security systems that are more advanced, efficient, and resilient.

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“…It achieved an average F1 score of 94.8%. Serial‐Parallel Convolutional Bidirectional Gated Recurrent Network Model incorporating Ensemble Classifiers (Zhang, Li, et al, 2022) also works on smart contract vulnerability detection. The model abbreviated as SPCBIG‐EC, focuses on 6 smart contract vulnerabilities and extracts features from the input for multivariate combinations while ensuring that it retains the location information and temporal structure.…”

Section: Literature Surveymentioning

confidence: 99%

A novel approach to detect fraud in Ethereum transactions using stacking

et al. 2023

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Ever since Ether is launched as a digital currency, its rise has been rapid. It is currently the second most valuable digital currency in the world. There are more than 1 million transactions happening on the Ethereum network every day, and this number is expected to continue to increase. Due to the increasing number of transactions, fraudulent transactions have also increased, which has resulted in a large amount of money being lost and has also destroyed the livelihoods of many individuals. Due to their similarity to valid transactions, it is extremely difficult to distinguish between them. Additionally, Ethereum's pseudo‐anonymity adds to the difficulty of identifying the parties involved. Since there are millions of transactions every day, it would be difficult to manually verify each one. Therefore, a mechanism for validating these transactions is needed. In this context, this paper proposes a novel approach to detecting fraudulent accounts associated with these transactions by implementing machine learning algorithms among the given set of transactions. We propose a framework for creating a stacking classifier by combining several standalone classification algorithms and creating a meta‐learner based on the output of each base algorithm. The algorithms include Logistic Regression, Naive Bayes, Decision Trees, Random Forests, AdaBoosts, KNNs, SVMs, and Gradient Boosts. As a result of combining these algorithms, a powerful classifier with the ability to detect fraudulent transactions. A variety of machine learning models were trained and evaluated on the test set using various metrics. Based on the results of the individual algorithm the Random Forest algorithm achieved the highest accuracy of 95.47%, followed by Gradient Boosting at 94.61% which is an ensemble algorithm using the boosting technique. The Stacking classifier that combines Multinomial Naive Bayes and Random Forest as the base learners and logistic regression as the Meta learner achieved the highest accuracy of 97.18% with an F1 score of 97.02%. Based on the results of all the stacking models developed, it is concluded that algorithms tend to perform better when combined properly. When compared to the other approaches, the proposed approach has outperformed the others, making it feasible in the real world to detect fraudulent transactions.

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

Cited by 27 publications

References 62 publications

The Impact of Input Types on Smart Contract Vulnerability Detection Performance Based on Deep Learning: A Preliminary Study

The Impact of Input Types on Smart Contract Vulnerability Detection Performance Based on Deep Learning: A Preliminary Study

Quantum Deep Neural Network Based Classification of Attack Vectors on the Ethereum Blockchain

A novel approach to detect fraud in Ethereum transactions using stacking

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