GRATDet:Smart Contract Vulnerability Detector Based on Graph Representation and Transformer

Smart contracts have led to more efficient development in finance and healthcare,but vulnerabilities in contracts pose high risks to their future applications.The current vulnerability detection methods for contracts are either based on fixed expert rules,which are inefficient,or rely on simplistic deep learning techniques that do not fully leverage contract semantic information.Therefore,there is ample room for improvement in terms of detection precision.To solve these problems,this paper proposes a vulnerability detector based on deep learning techniques,graph representation,and Transformer,called GRATDet.The method first performs swapping,insertion,and symbolization operations for contract functions,increasing the amount of small sample data.Each line of code is then treated as a basic semantic element,and information such as control and data relationships is extracted to construct a new representation in the form of a Line Graph(LG),which shows more structural features that differ from the serialized presentation of the contract.Finally,the node information and edge information of the graph are jointly learned using an improved Transformer-GP model to extract information globally and locally,and the fused features are used for vulnerability detection.The effectiveness of the method in reentrancy vulnerability detection is verified in experiments,where the F1 score reaches 95.16%,exceeding stateof-the-art methods.

DFE-GCN: Dual Feature Enhanced Graph Convolutional Network for Controversy Detection

With the development of social media and the prevalence of mobile devices,an increasing number of people tend to use social media platforms to express their opinions and attitudes,leading to many online controversies.These online controversies can severely threaten social stability,making automatic detection of controversies particularly necessary.Most controversy detection methods currently focus on mining features from text semantics and propagation structures.However,these methods have two drawbacks:1)limited ability to capture structural features and failure to learn deeper structural features,and 2)neglecting the influence of topic information and ineffective utilization of topic features.In light of these phenomena,this paper proposes a social media controversy detection method called Dual Feature Enhanced Graph Convolutional Network(DFE-GCN).This method explores structural information at different scales from global and local perspectives to capture deeper structural features,enhancing the expressive power of structural features.Furthermore,to strengthen the influence of topic information,this paper utilizes attention mechanisms to enhance topic features after each graph convolutional layer,effectively using topic information.We validated our method on two different public datasets,and the experimental results demonstrate that our method achieves state-of-the-art performance compared to baseline methods.On the Weibo and Reddit datasets,the accuracy is improved by 5.92%and 3.32%,respectively,and the F1 score is improved by 1.99%and 2.17%,demonstrating the positive impact of enhanced structural features and topic features on controversy detection.

The Detection of Fraudulent Smart Contracts Based on ECA-EfficientNet and Data Enhancement

With the increasing popularity of Ethereum,smart contracts have become a prime target for fraudulent activities such as Ponzi,honeypot,gambling,and phishing schemes.While some researchers have studied intelligent fraud detection,most research has focused on identifying Ponzi contracts,with little attention given to detecting and preventing gambling or phishing contracts.There are three main issues with current research.Firstly,there exists a severe data imbalance between fraudulent and non-fraudulent contracts.Secondly,the existing detection methods rely on diverse raw features that may not generalize well in identifying various classes of fraudulent contracts.Lastly,most prior studies have used contract source code as raw features,but many smart contracts only exist in bytecode.To address these issues,we propose a fraud detection method that utilizes Efficient Channel Attention EfficientNet(ECA-EfficientNet)and data enhancement.Our method begins by converting bytecode into Red Green Blue(RGB)three-channel images and then applying channel exchange data enhancement.We then use the enhanced ECA-EfficientNet approach to classify fraudulent smart contract RGB images.Our proposed method achieves high F1-score and Recall on both publicly available Ponzi datasets and self-built multi-classification datasets that include Ponzi,honeypot,gambling,and phishing smart contracts.The results of the experiments demonstrate that our model outperforms current methods and their variants in Ponzi contract detection.Our research addresses a significant problem in smart contract security and offers an effective and efficient solution for detecting fraudulent contracts.