A Smart Contract Vulnerability Detection Model Based on Syntactic and Semantic Fusion Learning

In recent years, smart contract vulnerability detection methods mostly view smart contract source code as natural language for processing, which cannot fully capture the semantic and structural features of the source code and has a high rate of false positives and missing positives. To improve the accuracy of vulnerability detection, this paper uses Graph Neural Network to obtain the semantic and structural information of the source code and Convolutional Neural Network to assist learning. We propose a graph neural network-based vulnerability detection model for smart contracts, which transforms smart contracts into control flow graphs, learns graph embedding using graph neural networks, and introduces Convolutional Neural Networks to learn the node order information of control flow graphs, and finally performs vulnerability detection using graph embedding and node order information. Experimenting on real datasets, our accuracy and F1 values are improved and the model can effectively detect smart contract vulnerabilities.

In recent years, with the great success of pre-trained language models, the pre-trained BERT model has been gradually applied to the field of source code understanding. However, the time cost of training a language model from zero is very high, and how to transfer the pre-trained language model to the field of smart contract vulnerability detection is a hot research direction at present. In this paper, we propose a hybrid model to detect common vulnerabilities in smart contracts based on a lightweight pre-trained language model BERT and connected to a bidirectional gate recurrent unit model. The downstream neural network adopts the bidirectional gate recurrent unit neural network model with a hierarchical attention mechanism to mine more semantic features contained in the source code of smart contracts by using their characteristics. Our experiments show that our proposed hybrid neural network model SolBERT-BiGRU-Attention is fitted by a large number of data samples with smart contract vulnerabilities, and it is found that compared with the existing methods, the accuracy of our model can reach 93.85%, and the Micro-F1 Score is 94.02%.

The financial property of Ethereum makes smart contract attacks frequently bring about tremendous economic loss. Method for effective detection of vulnerabilities in contracts imperative. Existing efforts for contract security analysis heavily rely on rigid rules defined by experts, which are labor-intensive and non-scalable. There is still a lack of effort that considers combining expert-defined security patterns with deep learning. This paper proposes EtherGIS, a vulnerability detection framework that utilizes graph neural networks (GNN) and expert knowledge to extract the graph feature from smart contract control flow graphs (CFG). To gain multi-dimensional contract information and reinforce the attention of vulnerability-related graph features, sensitive EVM instruction corpora are constructed by analyzing EVM underlying logic and diverse vulnerability triggering mechanisms. The characteristic of nodes and edges in a CFG is initially confirmed according to the corpora, generating the corresponding attribute graph. GNN is adopted to aggregate the whole graph's attribute and structure information, bridging the semantic gap between low-level graph features and high-level contract features. The feature representation of the graph is finally input into the graph classification model for vulnerability detection. Furthermore, automated machine learning (AutoML) is adopted to automate the entire deep learning process. Data for this research was collected from Ethereum to build up a dataset of six vulnerabilities for evaluation. Experimental results demonstrate that EtherGIS can productively detect vulnerabilities in Ethereum smart contracts in terms of accuracy, precision, recall, and F1-score. All aspects outperform the existing work.

The blockchain 2.0 age, marked by smart contract and Ethereum, has arrived couple years ago. Its technologies have expanded the application scenarios of blockchain technology and driven the boom of decentralized Finance. However, smart contract vulnerabilities and security issues are also emerging one after another. Hackers have exploited these vulnerabilities to cause huge economic losses. In recent years, a large amount of research on the analysis and detection of smart contract vulnerabilities has emerged, but there has been no common detection tool and corresponding test dataset. In this paper, we build GSVD dataset (Generalized Smart Contract Vulnerability Dataset) consisting four offline datasets using smart contracts on two chains, Polygon and BSC: two small Solidity datasets consisting of 153 labeled smart contract source codes, which can be used to test the performance of vulnerability mining tools; two large Solidity datasets consisting of 52,202 un labeled real smart contract source codes that can be used to verify the correctness of various theories and tools under a large number of real data conditions. At the same time, this paper integrates the scripting framework accompanying the GSVD dataset, which can execute a variety of popular automated vulnerability detection tools on top of these datasets and generate analysis results of contracts and potential vulnerabilities. We tested the Minor dataset under GSVD using three tools (Slither, Manticore, Mythril) that are kept up to date and found that the combined use of all tools detected 61.1% of labeled vulnerabilities, of which Mythril has the highest detection rate of 42.6%. It is not difficult to conclude that there`re still ample room for advancement for current smart contract vulnerability mining tools because of their underlying methods. Besides, our dataset can contribute to the ultimate target greatly by providing mining tools plenty real contracts information.

Smart contracts are programs running on blockchain. In recent years, due to the persistent occurrence of security-related accidents in smart contracts, the effective detection of vulnerabilities in smart contracts has received extensive attention from researchers and engineers. Machine learning-based vulnerability detection techniques have the advantage that they do not need expert rules for determining vulnerabilities. However, existing approaches cannot identify vulnerabilities when the versions of smart contract compilers are updated. In this paper, we propose OC-Detector (Opcode Clustering Detector), a smart contract vulnerability detection approach based on clustering opcode instructions. OC-Detector learns the characteristics of opcode instructions to cluster them and replaces opcode instructions belonging to the same cluster with the ID of the cluster. After that, the similarity between the contract under analysis and contracts in the vulnerability database is calculated to identify vulnerabilities. The experimental results demonstrate that OC-Detector improves the F1 value of detecting vulnerabilities from 0.04 to 0.40 compared to DC-Hunter, Securify, SmartCheck and Osiris. Additionally, compared to DC-Hunter, the F1 value is improved by 0.27 when detecting vulnerabilities in smart contracts compiled by different versions of compilers.

With the increasing popularity of Decentralized Applications (DApps) in blockchain, securing smart contracts has been a long-term, high-priority subject in the domain. Among the various research directions for vulnerability detection, fuzzing has received extensive attention because of its high effectiveness. However, with the increasing complexity of smart contracts, existing fuzzers may waste substantial time exploring locations irrelevant to smart contract vulnerabilities. In this article, we present FunFuzz, a function-oriented fuzzer, which is dedicatedly tailored for detecting smart contract vulnerability with high effectiveness and efficiency. The key observation in our research is that most smart contract vulnerabilities exist in specific functions rather than randomly distributed in all program code like other traditional software. To this end, unlike traditional fuzzers which mainly target code coverage, FunFuzz identifies risky functions while pruning non-risky ones in smart contracts. In this way, it significantly narrows down the exploration scope during the fuzzing process. In addition, FunFuzz employs three unique strategies to direct itself toward effectively discovering vulnerabilities specific to smart contracts (e.g., reentrancy, block dependency, and gasless send). Extensive experiments on 170 real-world contracts demonstrate that FunFuzz outperforms state-of-the-art fuzzers in terms of effectiveness and efficiency.

During software development and maintenance, vulnerability detection is an essential part of software quality assurance. Even though many program-analysis-based and machine-learning-based approaches have been proposed to automatically detect vulnerabilities, they rely on explicit rules or patterns defined by security experts and suffer from either high false positives or high false negatives. Recently, an increasing number of studies leverage deep learning techniques, especially Graph Neural Network (GNN), to detect vulnerabilities. These approaches leverage program analysis to represent the program semantics as graphs and perform graph analysis to detect vulnerabilities. However, they suffer from two main problems: (i) Existing GNN-based techniques do not effectively learn the structural and semantic features from source code for vulnerability detection. (ii) These approaches tend to ignore fine-grained information in source code. To tackle these problems, in this paper, we propose a novel vulnerability detection approach, named <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MGVD</small> (<bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</b> <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ultiple</small>-<bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G</b> <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">raph-Based</small> <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">V</b> <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ulnerability</small> <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D</b> <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">etection)</small>, to detect vulnerable functions. To effectively learn the structural and semantic features from source code, <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MGVD</small> uses three different ways to represent each function into multiple forms, i.e., two statement graphs and a sequence of tokens. Then we encode such representations to a three-channel feature matrix. The feature matrix contains the structural feature and the semantic feature of the function. And we add a weight allocation layer to distribute the weights between structural and semantic features. To overcome the second problem, <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MGVD</small> constructs each graph representation of the input function using multiple different graphs instead of a single graph. Each graph focuses on one statement in the function and its nodes denote the related statements and their fine-grained code elements. Finally, <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MGVD</small> leverages CNN to identify whether this function is vulnerable based on such feature matrix. We conduct experiments on 3 vulnerability datasets with a total of 30,341 vulnerable functions and 127,931 non-vulnerable functions. The experimental results show that our method outperforms the state-of-the-art by 9.68% – 10.28% in terms of F1-score.

This paper aims to explore the application of deep learning in smart contract vulnerabilities detection. Smart contracts are an essential part of blockchain technology and are crucial for developing decentralized applications. However, smart contract vulnerabilities can cause financial losses and system crashes. Static analysis tools are frequently used to detect vulnerabilities in smart contracts, but they often result in false positives and false negatives because of their high reliance on predefined rules and lack of semantic analysis capabilities. Furthermore, these predefined rules quickly become obsolete and fail to adapt or generalize to new data. In contrast, deep learning methods do not require predefined detection rules and can learn the features of vulnerabilities during the training process. In this paper, we introduce a solution called Lightning Cat which is based on deep learning techniques. We train three deep learning models for detecting vulnerabilities in smart contract: Optimized-CodeBERT, Optimized-LSTM, and Optimized-CNN. Experimental results show that, in the Lightning Cat we propose, Optimized-CodeBERT model surpasses other methods, achieving an f1-score of 93.53%. To precisely extract vulnerability features, we acquire segments of vulnerable code functions to retain critical vulnerability features. Using the CodeBERT pre-training model for data preprocessing, we could capture the syntax and semantics of the code more accurately. To demonstrate the feasibility of our proposed solution, we evaluate its performance using the SolidiFI-benchmark dataset, which consists of 9369 vulnerable contracts injected with vulnerabilities from seven different types.

The number of applications supported by blockchain smart contracts has been greatly increasing in recent years, with smart contracts now being used across several domains, such as the music industry, finance, and retail, to name a few. Despite being used in business-critical contexts, the number of security vulnerabilities in smart contracts has also been increasing, with many of them being exploited and resulting in huge financial and reputation losses. This is despite the enormous effort that is being placed into the research and development of vulnerability detection tools and techniques, which have also greatly increased in number and type in the last few years. Motivated by the recent increase in both vulnerabilities and vulnerability detection techniques, this paper reviews the latest research in smart contract vulnerability detection, emphasizing the techniques being used, the vulnerabilities targeted, and the characteristics of the dataset used for evaluating the technique. We mapped the vulnerabilities against two common vulnerability classification schemes (DASP and SWC) and performed a consolidated analysis. We identified the current research trends and gaps in each technique and highlighted future research opportunities in the field.

Vulnerable smart contract function locating based on Multi-Relational Nested Graph Convolutional Network

Information and communication technology based inter-organizational systems enable companies to integrate information and conduct business electronically across different parts of the organization. For organizations embracing blockchain, smart contracts provide automation and operational efficiency for inter-organizational systems. Initially utilised for financial transactions, smart contract are extended beyond banking and deployed in wide number of organizations. Smart contracts are regarded as self-executing type of contract consisting of agreement’s terms embedded directly into the code which plays a vital role in operability for inter-organizational systems, however, smart contract vulnerabilities can arise due to programming errors, leading to security issues. The effects of smart contract vulnerabilities can be significant, including loss of funds, unauthorized access to sensitive information, manipulation of data, and loss of trust in the application leading to catastrophic financial losses followed by legal implications for an organization based on blockchain technology. The goal of smart contracts exploiting vulnerabilities is to discover and eliminate potential security vulnerabilities in smart contract code prior to it being deployed. Detecting vulnerabilities in a timely manner helps to prevent financial losses, unauthorized access, and data manipulation. In order to provide a robust solution to detect vulnerabilities in smart contracts, the proposed methodology presents a novel approach for rapid detection of vulnerabilities by integrating genetic algorithm with isolation forest. Furthermore, enhancing smart contract vulnerability identification with higher accuracy and false-positive rate provides a reliable gateway for organizations to adopt blockchain.

Smart contracts are decentralized applications running on the blockchain to meet various practical scenario demands. The increasing number of security events regarding smart contracts have led to huge pecuniary losses and destroyed the ecological stability of contract layer on the blockchain. Faced with the increasing quantity of contracts, it is an emerging issue to effectively and efficiently detect vulnerabilities in smart contracts. Existing methods of detecting vulnerabilities in smart contracts like Oyente mainly employ symbolic execution. This method is very time-consuming, as the symbolic execution requires the exploration of all executable paths in a contract. In this work, we propose an efficient model for the detection of vulnerabilities in Ethereum smart contracts with machine learning techniques. The model is able to effectively and fast detect vulnerabilities based on the patterns learned from training samples. Our model is evaluated on 49502 real-world smart contracts and the results verify its effectiveness and efficiency.

The classification of the smart contract can effectively reduce the search space and improve retrieval efficiency. The existing classification methods are based on natural language processing technologies. Because the processing of source code by these technologies lacks extraction and processing in the software engineering field, there is still a lot of room for improvement in their methods of feature extraction. Therefore, this paper proposes a multi-feature fusion method for smart contract classification (MFF-SC) based on the code processing technology. From the source code perspective, source code processing method and attention mechanism are used to extract local code features. Structure-based traversal method are used to extract global code features from abstract syntax tree. Local and global code features introduce attention mechanism to generate code semantic features. From the perspective of account transaction, the feature of account transaction is extracted by using TransR. Next, the code semantic features and account transaction features generate smart contract semantic features by an attention mechanism. Finally, the smart contract semantic features are fed into a stacked denoising autoencoder and a softmax classifier for classification. Compared with other baselines and variants, experimental results on a real-world dataset show that MFF-SC achieves an accuracy rate of 83.9%.

Smart contracts are frequently targeted by hackers because they hold large amounts of money and cannot be modified once they are published. Existing detection methods mainly focus on known vulnerabilities with clear features and cannot deal with unknown vulnerabilities. As a consequence, proposing a method for detecting unknown vulnerabilities in smart contracts represents a significant advancement in the field of smart contract security. Aiming at this problem, based on the idea that the opcode sequences of transactions containing unknown vulnerabilities have similarities to the opcode sequences of transactions containing known vulnerabilities, we propose a novel approach for unknown vulnerability detection in smart contracts using a CNN-BiLSTM model. Our model determines whether a vulnerability is unknown by detecting the opcode sequence representing the entire execution process of a transaction. Experimental results with the opcode sequences of transactions show that the model can achieve 82.86% accuracy and 83.63% F1-score.KeywordsSmart contractsUnknown vulnerabilitiesVulnerability detectionCNN-BiLSTMOpcode sequences

With the continuous development of blockchain technology, thousands of smart contracts have been deployed on the blockchain, and the number of smart contract vulnerabilities has increased significantly. In the task of smart contract vulnerability detection, fuzz testing methods are usually used for detection. Existing AFL-based methods are inefficient in generating test cases that meet complex path constraints. This study addresses the limitations of traditional fuzz testing techniques in detecting vulnerabilities related to strictly constrained conditional branches in Ethereum smart contracts. To overcome this challenge, we propose a hybrid framework that combines static semantic analysis with adaptive dynamic fuzz testing and combines a lightweight heuristic seed selection mechanism to prioritize path-sensitive mutations. Our method adopts semantic-aware operators to guide targeted exploration of protected execution paths while dynamically optimizing energy allocation among test cases. Experimental evaluation on benchmark contracts shows that compared with baseline methods, the proposed framework achieves significantly improved branch coverage and accelerated vulnerability detection, especially for critical security vulnerabilities such as reentrancy and arithmetic exceptions, without sacrificing detection accuracy. The results verify the effectiveness of our method in balancing exploration efficiency and analysis rigor for blockchain-oriented security verification.