Huaiguang Wu

Ensuring the absence of vulnerabilities or flaws in smart contracts before their deployment is crucial for the smooth progress of subsequent work. Existing detection methods heavily rely on expert rules, resulting in low robustness and accuracy. Therefore, we propose EDSCVD, an enhanced deep learning vulnerability detection model based on dual-channel networks. Firstly, the contract fragments are preprocessed by BERT into the required word embeddings. Next, we utilized adversarial training FGM to the word embeddings to generate perturbations, thereby producing symmetric adversarial samples and enhancing the robustness of the model. Then, the dual-channel model combining BiLSTM and CNN is utilized for feature training to obtain more comprehensive and symmetric information on temporal and local contract features.Finally, the combined output features are passed through a classifier to classify and detect contract vulnerabilities. Experimental results show that our EDSCVD exhibits excellent detection performance in the detection of classical reentrancy vulnerabilities, timestamp dependencies, and integer overflow vulnerabilities.

Representation-based methods have found widespread applications in various classification tasks. However, these methods cannot deal effectively with imbalanced data scenarios. They tend to neglect the importance of minority samples, resulting in bias toward the majority class. To address this limitation, we propose a density-based discriminative nonnegative representation approach for imbalanced classification tasks. First, a new class-specific regularization term is incorporated into the framework of a nonnegative representation based classifier (NRC) to reduce the correlation between classes and improve the discrimination ability of the NRC. Second, a weight matrix is generated based on the hybrid density information of each sample’s neighbors and the decision boundary, which can assign larger weights to minority samples and thus reduce the preference for the majority class. Furthermore, the resulting model can be efficiently optimized through the alternating direction method of multipliers. Extensive experimental results demonstrate that our proposed method is superior to numerous state-of-the-art imbalanced learning methods.

A smart contract is a special form of computer program that runs on a blockchain and provides a new way to implement financial and business transactions in a conflict-free and transparent environment. In blockchain systems such as Ethereum, smart contracts can handle and autonomously transfer assets of considerable value to other parties. Hence, it is particularly important to ensure that smart contracts function as intended since bugs or vulnerabilities may lead, and indeed have led, to substantial economic losses and erosion of trust for blockchain. While a number of approaches and tools have been developed to find vulnerabilities, formal methods present the highest level of confidence in the security of smart contracts. In this paper, we propose a formal solution to model a smart contract based on colored Petri nets (CPNs). Herein, we focus on the most common type of security bugs in smart contract, i.e., reentrancy bugs, which led to a serious financial loss of around USD 34 million for the Cream Finance project in 2021. We present a hierarchical CPN modelling method to analyze potential security vulnerabilities at the contract’s source code level. Then, modeling analysis methods such as correlation matrix, state space report and state space graph generated via CPN Tools simulation are exploited for formal analysis of smart contracts. The example shows the full state space and wrong path in accordance with our expected results. Finally, the conclusion was verified on the Ethereum network based on the Remix platform.

Entity resolution, accurately identifying various representations of the same real-world entities, is a crucial part of data integration systems. While existing learning-based models can achieve good performance, the models are extremely dependent on the quantity and quality of training data. In this paper, the MixER model is proposed to alleviate these problems. The MixER utilizes our newly designed data augmentation method called EMix. The EMix can map discrete entity records to continuous latent space variables (e.g., probability distributions) and then linearly interpolate entity records in latent space to generate many augmented training samples. The matching model is further optimized based on the augmented data to strengthen its generalization capability. The MixER model achieves significant strengths in the data sensitivity experiments when training data is below 50. In robustness experiments, the MixER model presents an absolute performance advantage when the label noise exceeds 20%. In addition, ablation experiments demonstrate that the newly developed EMix can effectively improve the generalization ability of the matching model. The overall experimental results prove that the MixER model exhibited excellent data sensitivity and robustness over the current state-of-the-art methods.

A smart contract, as an important part of blockchain technology, has attracted considerable interest from both industry and academia. It provides the basis for the realization of a variety of practical blockchain applications and plays a crucial role in the blockchain ecosystem. While it also holds a large number of digital assets, the frequent occurrence of smart contract vulnerabilities have caused huge economic losses and destroyed the blockchain-based credit system. Currently, the security and reliability of smart contracts have become a new focus of research, and there are a number of smart contract vulnerability detection methods, such as traditional detection tools based on static or dynamic analysis. However, most of them often rely on expert rules, and therefore have poor scalability and high false negative and false positive rates. Recent deep learning methods alleviate this issue, but without considering the semantic information and context of source code. To this end, we propose a hybrid attention mechanism (HAM) model to detect security vulnerabilities in smart contracts. We extract code fragments from the source code, which focus on key points of vulnerability. We conduct extensive experiments on two public smart contract datasets (a total of 24,957 contracts). Empirical results show remarkable accuracy improvement over the state-of-the art methods on five kinds of vulnerabilities, where the detection accuracy could achieve 93.36%, 80.85%, 82.56%, 85.62%, and 82.19% for reentrancy, arithmetic vulnerability, unchecked return value, timestamp dependency, and tx.origin, respectively.

The Blockchain 2.0 era integrated with smart contract along with its platforms and applications have experienced explosive growth in recent years. However, many smart contracts deployed in practice are prone to errors and cannot be modified due to the immutability of the blockchain. In light of this, it is vital to ensure the security of correctness of smart contracts before deployment. This paper proposes a formal modeling method based on Timed Colored Petri net (TCPN) to anlyze smart contracts with time constrains. We apply this formalism to a concrete voting contract system. Its functional requirements are formalized by the improved computing timing logic ASK-CTL with ML (Meta Language). Simulation and experiment show that the security and reliability of the voting contract can be effectively guaranteed.

Entity resolution (ER), precisely identifying different representations of the same real-world entities, is critical for data integration. The ER question has been studied for many years, and many methods have been proposed to solve it. Although deep learning has achieved good performance in ER tasks, there are some challenges regarding manual labelling and model transfer. This paper proposes a novel ER model, Transformer-based Denoising Adversarial Variational Entity Resolution (TdavER). For entity embedding, we develop an unsupervised entity embedding model based on denoising autoencoders and pre-trained language models, which takes corrupted input as training data to motivate the encoder to generate rather stable and robust high-quality entity representations. Furthermore, we propose an unsupervised entity feature transformation model based on adversarial variational autoencoders to remove the constraints on entity representations from training data. This transformation model converts low-level entity embeddings to high-level probability distributions, which are not constrained by the source data and contain deep similarity features. To better implement the feature transformation, we adopt adversarial networks to optimize the variational autoencoder's training process and help it learn the correct posterior distribution. Extensive experiments confirm that the performance of our proposed TdavER is comparable with the current state-of-the-art ER methods and that its entity feature transformation model is transferable.