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GRATDet:Smart Contract Vulnerability Detector Based on graph representation and Transformer

Computers, Materials & Continua 2023年第8期76卷 1439-1462页

作者： Peng Gong Wenzhong Yang Liejun Wang Fuyuan Wei KeZiErBieKe HaiLaTi Yuanyuan Liao College of Information Science and Engineering Xinjiang UniversityUrumqi830000China Key Laboratory of Signal Detection and Processing in Xinjiang Uygur Autonomous Region Xinjiang UniversityUrumqi830000China Key Laboratory of Multilingual Information Technology in Xinjiang Uygur Autonomous Region Xinjiang UniversityUrumqi830000China

Smart contracts have led to more efficient development in finance and healthcare,but vulnerabilities in contracts pose high risks to their future *** 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 ***,there is ample room for improvement in terms of detection *** solve these problems,this paper proposes a vulnerability detector based on deep learning techniques,graph representation,and Transformer,called *** method first performs swapping,insertion,and symbolization operations for contract functions,increasing the amount of small sample *** 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 ***,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 *** effectiveness of the method in reentrancy vulnerability detection is verified in experiments,where the F1 score reaches 95.16%,exceeding stateof-the-art methods.

关键词： Vulnerability detection smart contract graph representation deep learning source code

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Improving Centralized Path Calculation Based on graph Compression

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China Communications 2018年第6期15卷 120-124页

作者： Zhenglian Li Lixin Ji Ruiyang Huang Shuxin Liu National Digital Switching System Engineering&Technological R&D Center ZhengzhouChina

Shortest-path calculation on weighted graphs are an essential operation in computer networks. The performance of such algorithms has become a critical challenge in emerging software-defined networks(SDN),since SDN controllers need to centralizedly perform a shortest-path query for every flow,usually on large-scale network. Unfortunately,one of the challenges is that current algorithms will become incalculable as the network size increases. Therefore, inspired by the compression graph in the field of compute visualization,we propose an efficient shortest path algorithm by compressing the original big network graph into a small one, but the important graph properties used to calculate path is reserved. We implement a centralized version of our approach in SDN-enabled network,and the evaluations validate the improvement compared with the well-known algorithms.

关键词： graph representation path compression shortest path

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graph neural network based approach Che upo to automatically assigning common weakness enumeration identifiers for vulnerabilities

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Cybersecurity 2024年第3期7卷 1-15页

作者： Peng Liu Wenzhe Ye Haiying Duan Xianxian Li Shuyi Zhang Chuanjian Yao Yongnan Li Key Lab of Education Blockchain and Intelligent Technology Ministry of EducationGuangxi Normal UniversityGuilin 541004China School of Software Beihang UniversityBeijing 100000China School of National Security People's Public Security University of ChinaBeijing 1000000China Guangxi Key Lab of Multi-Source Information Mining and Security Guangxi Normal UniversityGuilin 541004China

Vulnerability reports are essential for improving software security since they record key information on *** a report,CWE denotes the weakness of the vulnerability and thus helps quickly understand the cause of the ***,CWE assignment is useful for categorizing newly discovered *** this paper,we propose an automatic CwE assignment method with graph neural ***,we prepare a dataset that contains 3394 real world vulnerabilities from Linux,OpenSSL,Wireshark and many other software ***,we extract state-ments with vulnerability syntax features from these vulnerabilities and use program slicing to slice them according to the categories of syntax *** top of slices,we represent these slices with graphs that characterize the data dependency and control dependency between ***,we employ the graph neural networks to learn the hidden information from these graphs and leverage the Siamese network to compute the similarity between vulnerability functions,thereby assigning CWE IDs for these *** experimental results show that the proposed method is effective compared to existing methods.

关键词： Vulnerability categorization CWE graph representation GNN

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