Improving Ponzi Scheme Contract Detection Using Multi-Channel TextCNN and Transformer

Dai, Honghua; Yu, Xiao; Hu, Wenhua; Xie, Zheng; Tan, Cheng

doi:10.3390/s21196417

Cited by 33 publications

(10 citation statements)

References 57 publications

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“…To address the problems of account transaction-based illegal transaction detection methods, researchers [28,29] have used smart contract code-based Ponzi detection methods, which can detect Ponzi schemes before the smart contract code is first released. Chen et al [30], for example, explore contract trading rules by analysing currency flow graphs, smart contract creation and smart contract invocation for transactions on Ethereum.…”

Section: A Smart Contract Code Based Approach To Illegal Transaction ...mentioning

confidence: 99%

Transformer-based comparative multi-view illegal transaction detection

et al. 2023

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In recent years, as the Ether platform has grown by leaps and bounds. Numerous unscrupulous individuals have used illegal transaction to defraud large sums of money, causing billions of dollars of losses to investors worldwide. Facing the endless stream of the illegal transaction based on Ether smart contracts problems, such as illegal transaction, money laundering, financial fraud, phishing. Currently, illegal transaction are only detected by a single view of the smart contract’s contract code view feature and account transaction view feature, which is not only incomplete, but also not fully representative of the smart contract’s features. More importantly, the single view detection model cannot accurately capture the global structure and semantic features between the Tokens of the view features. In this case, it is particularly important that all view features are shared among themselves. In this paper, we investigate a Transformer-based model for contrasting illegal transaction detection networks under multiple views (TranMulti-View Net). The model in this paper is based on Transformer to learn a multi-view fusion representation, which aims to maximise the fusion of the interaction information of different view features under the same condition. In this model we first use the Transformer model to learn global structure and semantic features from a sequence of Tokens tokenised by a view, capturing the remote dependencies of Tokens in the view features, and then we share the contract code view features and the account transaction view features across all views to learn important semantic information between views from each other. In addition, we find that the approach of semi-supervised training of multi-view features using contrast learning outperforms the scheme of prediction based on direct fusion of different view features, resulting in stronger correlation between view features. As a result, the underlying semantic information can be captured more accurately, leading to more accurate predictions of illegal transaction. The experimental results show that our proposed TranMulti-View Net obtains good detection results with a Precision score of 98%.

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Section: A Smart Contract Code Based Approach To Illegal Transaction ...mentioning

confidence: 99%

Transformer-based comparative multi-view illegal transaction detection

et al. 2023

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“…When checking the top 20% LOC according to the predicted result of the EADP model, the software testing team inspects n software modules and finds p actual defective modules with q defects. In our experiments, we utilise several evaluation measures that are commonly adopted in both the software engineering [92][93][94] and machine learning [95][96][97][98][99][100]. Precision@20% is the ratio between the number of actual defective modules and the number of predicted defective modules in the top 20% LOC.…”

Section: Effort-aware Evaluation Metricsmentioning

confidence: 99%

Revisiting ‘revisiting supervised methods for effort‐aware cross‐project defect prediction’

Yang

Keung

et al. 2023

IET Software

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Effort-aware cross-project defect prediction (EACPDP), which uses cross-project software modules to build a model to rank within-project software modules based on the defect density, has been suggested to allocate limited testing resource efficiently. Recently, Ni et al. proposed an EACPDP method called EASC, which used all cross-project modules to train a model without considering the data distribution difference between cross-project and within-project data. In addition, Ni et al. employed the different defect density calculation strategies when comparing EASC and baseline methods. To explore the effective defect density calculation strategies and methods on EACPDP, the authors compare four data filtering methods and five transfer learning methods with EASC using four commonly used defect density calculation strategies. The authors use three classification evaluation metrics and seven effort-aware metrics to assess the performance of methods on 11 PROMISE datasets comprehensively. The results show that (1) The classification before sorting (CBS+) defect density calculation strategy achieves the best overall performance. (2) Using balanced distribution adaption (BDA) and joint distribution adaptation (JDA) with the K-nearest neighbour classifier to build the EACPDP model can find 15% and 14.3% more defective modules and 11.6% and 8.9% more defects while achieving the acceptable initial false alarms (IFA). (3) Better comprehensive classification performance of the methods can bring better EACPDP performance to some extent. (4) A flexible adjustment of the defect threshold λ of the CBS+ strategy contribute to different goals. In summary, the authors recommend researchers and practitioners use to BDA and JDA with the CBS+ strategy to build the EACPDP model. K E Y W O R D Sdata mining, quality assurance, software engineering, software maintenance, software metrics, software quality | INTRODUCTIONComputer software is widely used in various industries in society today, which may fail with quality problems. As software plays an increasingly important role in various fields, ensuring software reliability is one of the issues people are more concerned about [1][2][3]. Software defects are a potential sources of errors, failures, and crashes of associated systems. However, the increase in the scale of the software makes defect inspection and fixing more time-consuming. Worse yet, softwareThis is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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“…In our empirical study, we use the three threshold-dependent evaluation metrics (Precision, Recall, and F-measure (F1)) and one threshold-independent evaluation metric (Matthews correlation coefficient, MCC) to evaluate the performance of CSD models. The metrics are widely used in both software engineering studies [64][65][66][67][68][69][70][71] and artificial intelligence researches. [72][73][74][75] In the binary classification problem, these four evaluation metrics can be calculated according to a confusion matrix, as shown in Table 4.…”

Section: Performance Measuresmentioning

confidence: 99%

On the relative value of imbalanced learning for code smell detection

Zou

Keung

et al. 2023

Softw Pract Exp

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SummaryMachine learning‐based code smell detection (CSD) has been demonstrated to be a valuable approach for improving software quality and enabling developers to identify problematic patterns in code. However, previous researches have shown that the code smell datasets commonly used to train these models are heavily imbalanced. While some recent studies have explored the use of imbalanced learning techniques for CSD, they have only evaluated a limited number of techniques and thus their conclusions about the most effective methods may be biased and inconclusive. To thoroughly evaluate the effect of imbalanced learning techniques for machine learning‐based CSD, we examine 31 imbalanced learning techniques with seven classifiers to build CSD models on four code smell data sets. We employ four evaluation metrics to assess the detection performance with the Wilcoxon signed‐rank test and Cliff's . The results show that (1) Not all imbalanced learning techniques significantly improve detection performance, but deep forest significantly outperforms the other techniques on all code smell data sets. (2) SMOTE (Synthetic Minority Over‐sampling TEchnique) is not the most effective technique for resampling code smell data sets. (3) The best‐performing imbalanced learning techniques and the top‐3 data resampling techniques have little time cost for code smell detection. Therefore, we provide some practical guidelines. First, researchers and practitioners should select the appropriate imbalanced learning techniques (e.g., deep forest) to ameliorate the class imbalance problem. In contrast, the blind application of imbalanced learning techniques could be harmful. Then, better data resampling techniques than SMOTE should be selected to preprocess the code smell data sets.

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Improving Ponzi Scheme Contract Detection Using Multi-Channel TextCNN and Transformer

Cited by 33 publications

References 57 publications

Transformer-based comparative multi-view illegal transaction detection

Transformer-based comparative multi-view illegal transaction detection

Revisiting ‘revisiting supervised methods for effort‐aware cross‐project defect prediction’

On the relative value of imbalanced learning for code smell detection

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