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Multiclass vulnerability and clone detection in Ethereum smart contracts using Block-wise abstract syntax tree based Federated Graph Neural Networks

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COMPUTERS & ELECTRICAL ENGINEERING 2025年 123卷

作者： Sharma, Shruti Ratmele, Ankur Seth, Abhay Deep SVKMs NMIMS Dept Comp Engn STME Indore 453112 Madhya Pradesh India

Smart contracts on blockchain networks autonomously execute applications based on predefined conditions, making their security-critical due to the potential for significant financial losses from vulnerabilities. Current vulnerability detection algorithms commonly rely on expert- defined rules, which are prone to errors and insufficient for identifying complex vulnerability patterns. Given the immutability of smart contracts post-deployment, ensuring security before deployment is essential. This research presents Block-wise abstract syntax tree based Federated Graph Neural Networks (BAST-FeGNN), a novel approach combining block-wise abstract syntax tree and Federated Graph Neural Networks (FeGNN) to detect code clones and multiclass vulnerabilities in Ethereum smart contracts. The BAST-FeGNN method operates in three stages: it first extracts security-related patterns from the base code using an abstract syntax tree;then, it constructs and normalizes a contract graph using FeGNN to capture critical nodes, analyze data and control flows. This integration of graph-based feature extraction with pattern matching allows precise detection of vulnerabilities like access control issues, reentrancy, and unchecked calls, as well as identifying code clones. Finally, the method pools these features for comprehensive vulnerability detection. BAST-FeGNN significantly enhances vulnerability detection accuracy and scalability, outperforming existing models with an accuracy of 95.35%, recall of 95.58%, F1-score of 95.80%, and precision of 96.10%, making it a robust solution for securing blockchain applications.

关键词： abstract syntax tree Ethereum smart contracts Federated graph neural network Vulnerability and clone detection

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A Heterogeneous Graph to abstract syntax tree Framework for Text-to-SQL

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2023年第11期45卷 13796-13813页

作者： Cao, Ruisheng Chen, Lu Li, Jieyu Zhang, Hanchong Xu, Hongshen Zhang, Wangyou Yu, Kai Shanghai Jiao Tong Univ X LANCE Lab MoE Key Lab Artificial Intelligence Dept Comp Sci & EngnAI Inst Shanghai 200240 Peoples R China

Text-to-SQL is the task of converting a natural language utterance plus the corresponding database schema into a SQL program. The inputs naturally form a heterogeneous graph while the output SQL can be transduced into an abstract syntax tree (AST). Traditional encoder-decoder models ignore higher-order semantics in heterogeneous graph encoding and introduce permutation biases during AST construction, thus incapable of exploiting the refined structure knowledge precisely. In this work, we propose a generic heterogeneous graph to abstract syntax tree (HG2AST) framework to integrate dedicated structure knowledge into statistics-based models. On the encoder side, we leverage a line graph enhanced encoder (LGESQL) to iteratively update both node and edge features through dual graph message passing and aggregation. On the decoder side, a grammar-based decoder first constructs the equivalent SQL AST and then transforms it into the desired SQL via post-processing. To avoid over-fitting permutation biases, we propose a golden tree-oriented learning (GTL) algorithm to adaptively control the expanding order of AST nodes. The graph encoder and tree decoder are combined into a unified framework through two auxiliary modules. Extensive experiments on various text-to-SQL datasets, including single/multi-table, single/cross-domain, and multilingual settings, demonstrate the superiority and broad applicability.

关键词： Structured Query Language Decoding Databases Syntactics Semantics Task analysis Computational modeling abstract syntax tree grammar-based constrained decoding heterogeneous graph neural network knowledge-driven natural language processing permutation invariant problem text -to-SQL

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Node Embedding of the abstract syntax tree for Source Code Representation 39th

Node Embedding of the Abstract Syntax Tree for Source Code R...

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39th CCF National Conference on Computer Applications

作者： Chen, Chang-Feng Zain, Azlan Mohd Zhou, Kai-Qing Jishou Univ Coll Comp Sci & Engn Jishou 416000 Peoples R China Univ Teknol Malaysia Fac Comp Skudai 80310 Johor Malaysia Jishou Univ Sch Commun & Elect Engn Jishou 416000 Peoples R China

ISBN: (纸本)9789819796700;9789819796717

Source code representation has garnered significant attention owing to its critical role in solving software engineering problems. There are various methodologies for representing code, and the abstract syntax tree (AST) is one of the most widely used techniques. However, current AST-based approaches need to pay more attention to the impact of AST generation, resulting in high duplication of the tree structure. Additionally, most AST-based methods struggle to balance homophily and structural equivalence in sequence sampling while failing to express semantic information in node feature learning. These limitations lead to poor source code representation and reduced performance in solving software engineering problems. To address these challenges, we propose a novel model that combines a deduplication algorithm, a hybrid sampling strategy, and an optimized skip-gram feature learning approach to represent source code. To evaluate the efficacy of our proposed model, we conduct experiments on OJ datasets for program classification. Our results demonstrate that the proposed model reduces the duplication of the tree structure and improves the representation performance of source code compared with other methods.

关键词： Source code representation abstract syntax tree skip-gram code classification

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Multi-language Webshell Detection based on abstract syntax tree and treeLSTM 27

Multi-language Webshell Detection based on Abstract Syntax T...

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27th International Conference on Computer Supported Cooperative Work in Design (CSCWD)

作者： Shang, Mengchuan Han, Xueying Zhao, Changzhi Cui, Zelin Du, Dan Jiang, Bo Chinese Acad Sci Inst Informat Engn Beijing Peoples R China Univ Chinese Acad Sci Sch Cyber Secur Beijing Peoples R China

ISBN: (纸本)9798350349184;9798350349191

Webshell is a command execution environment existing in web containers, which is used by attackers to remotely control servers and illegally access website resources. Accurately detecting Webshells is of great significance for maintaining web security. Current research faces several challenges. On the one hand, in order to evade detection, Webshells use a large amount of obfuscation, and existing research methods often use source code or opcode, which cannot fully utilize the semantic and syntactic information of Webshell code. On the other hand, Webshells can be constructed using any web application programming language, while most existing methods only detect one or a few types of Webshells. This paper proposes a novel approach called WS-tree, which effectively utilizes the semantics and syntax of Webshells by using abstract syntax tree as input features. The treeLSTM model is used as an encoder to handle node relationships in the syntax tree, thereby achieving the detection of obfuscated and multi-language Webshells. We also propose a new dataset of Webshells containing obfuscated and non-obfuscated to prevent dataset leakage. Extensive experiments demonstrate that our proposed model performs better than the state-of-the-art baselines under different webshell programming languages and improves model generalizability.

关键词： Webshell detection abstract syntax tree treeLSTM Multi-language Deep learning

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TRIDENT: Detecting SQL Injection Attacks via abstract syntax tree-based Neural Network 24

TRIDENT: Detecting SQL Injection Attacks via Abstract Syntax...

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39th ACM/IEEE International Conference on Automated Software Engineering (ASE)

作者： Li, Yuanlin Xu, Zhiwei Zhou, Min Wan, Hai Zhao, Xibin Tsinghua Univ Sch Software BNRist KLISS Beijing Peoples R China

ISBN: (数字)9798400712487

ISBN: (纸本)9798400712487

SQL injection attacks have posed a significant threat to web applications for decades. They obfuscate malicious codes into natural SQL statements so as to steal sensitive data, making them difficult to detect. Generally, malicious signals can be identified by using the contextual information of SQL statements. Such contextual information, however, is not always easily captured. Due to the fact that SQL as a formal language is highly structured, two tokens that are spatially far away may be semantically very close. An effective approach thus should take the structural feature of SQL statements into account when modeling their contextual information. In this paper, we present a novel abstract syntax tree-based neural network approach named TRIDENT for effectively detecting SQL injection attacks. Benefiting from the structural feature delivered by ASTs, TRIDENT realizes superior modeling of contextual information via tree-based positional embedding and well-designed neural networks. TRIDENT is widely evaluated on a public SQL injection dataset and an adversarial sample dataset. The results demonstrate that TRIDENT can significantly outperform the baselines.

关键词： SQL injection detection abstract syntax tree Neural network

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ATOM: Commit Message Generation Based on abstract syntax tree and Hybrid Ranking

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IEEE TRANSACTIONS ON SOFTWARE ENGINEERING 2022年第5期48卷 1800-1817页

作者： Liu, Shangqing Gao, Cuiyun Chen, Sen Nie, Lun Yiu Liu, Yang Nanyang Technol Univ Sch Comp Sci & Engn Singapore 639798 Singapore Harbin Inst Technol Sch Comp Sci & Technol Shenzhen 518055 Peoples R China Tianjin Univ Coll Intelligence & Comp Tianjin 300350 Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China

Commit messages record code changes (e.g., feature modifications and bug repairs) in natural language, and are useful for program comprehension. Due to the frequent updates of software and time cost, developers are generally unmotivated to write commit messages for code changes. Therefore, automating the message writing process is necessitated. Previous studies on commit message generation have been benefited from generation models or retrieval models, but the code structure of changed code, i.e., AST, which can be important for capturing code semantics, has not been explicitly involved. Moreover, although generation models have the advantages of synthesizing commit messages for new code changes, they are not easy to bridge the semantic gap between code and natural languages which could be mitigated by retrieval models. In this paper, we propose a novel commit message generation model, named ATOM, which explicitly incorporates the abstract syntax tree for representing code changes and integrates both retrieved and generated messages through hybrid ranking. Specifically, the hybrid ranking module can prioritize the most accurate message from both retrieved and generated messages regarding one code change. We evaluate the proposed model ATOM on our dataset crawled from 56 popular Java repositories. Experimental results demonstrate that ATOM increases the state-of-the-art models by 30.72 percent in terms of BLEU-4 (an accuracy measure that is widely used to evaluate text generation systems). Qualitative analysis also demonstrates the effectiveness of ATOM in generating accurate code commit messages.

关键词： Syntactics Semantics Atomic measurements Hybrid power systems Benchmark testing Writing Java Commit message generation code changes abstract syntax tree

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Exploring the Effectiveness of abstract syntax tree Patterns for Algorithm Recognition 4

Exploring the Effectiveness of Abstract Syntax Tree Patterns...

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4th International Conference on Code Quality (ICCQ)

作者： Neumuller, Denis Straub, Raphael Sihler, Florian Tichy, Matthias Univ Ulm Ulm Germany

ISBN: (纸本)9798350366464;9798350366471

The automated recognition of algorithm implementations can support many software maintenance and reengineering activities by providing knowledge about the concerns present in the code base. Moreover, recognizing inefficient algorithms like Bubble Sort and suggesting superior alternatives from a library can help in assessing and improving the quality of a system. Approaches from related work suffer from usability as well as scalability issues and their accuracy is not evaluated. In this paper, we investigate how well our approach based on the abstract syntax tree of a program performs for automatic algorithm recognition. To this end, we have implemented a prototype consisting of: A domain-specific language designed to capture the key features of an algorithm and used to express a search pattern on the abstract syntax tree, a matching algorithm to find these features, and an initial catalog of "ready to use" patterns. To create our search patterns we performed a web search using the algorithm name and described key features of the found reference implementations with our domain-specific language. We evaluate our prototype on a subset of the BigCloneEval benchmark containing algorithms like Fibonacci, Bubble Sort, and Binary Search. We achieve an average F-1-score of 0.74 outperforming the large language model Codellama which attains 0.35. Additionally, we use multiple code clone detection tools as a baseline for comparison, achieving a recall of 0.62 while the best-performing tool reaches 0.20.

关键词： algorithm recognition program comprehension pattern matching abstract syntax tree domain-specific language reverse engineering maintenance

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Fine-Grained Code Clone Detection with Block-Based Splitting of abstract syntax tree 2023

Fine-Grained Code Clone Detection with Block-Based Splitting...

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32nd ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA)

作者： Hu, Tiancheng Xu, Zijing Fang, Yilin Wu, Yueming Yuan, Bin Zou, Deqing Jin, Hai Huazhong Univ Sci & Technol Sch Cyber Sci & Engn Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Hubei Key Lab Distributed Syst Secur Hubei Engn Res Ctr Big Data SecurCluster & Grid Natl Engn Res Ctr Big Data Technol & SystServ Co Wuhan Peoples R China Nanyang Technol Univ Singapore Singapore Huazhong Univ Sci & Technol Sch Comp Sci Wuhan 430074 Peoples R China

ISBN: (纸本)9798400702211

Code clone detection aims to find similar code fragments and gains increasing importance in the field of software engineering. There are several types of techniques for detecting code clones. Text-based or token-based code clone detectors are scalable and efficient but lack consideration of syntax, thus resulting in poor performance in detecting syntactic code clones. Although some tree-based methods have been proposed to detect syntactic or semantic code clones with decent performance, they are mostly time-consuming and lack scalability. In addition, these detection methods can not realize fine-grained code clone detection. They are unable to distinguish the concrete code blocks that are cloned. In this paper, we design Tamer, a scalable and fine-grained tree-based syntactic code clone detector. Specifically, we propose a novel method to transform the complex abstract syntax tree into simple subtrees. It can accelerate the process of detection and implement the fine-grained analysis of clone pairs to locate the concrete clone parts of the code. To examine the detection performance and scalability of Tamer, we evaluate it on a widely used dataset BigCloneBench. Experimental results show that Tamer outperforms ten state-of-the-art code clone detection tools (i.e., CCAligner, SourcererCC, Siamese, NIL, NiCad, LVMapper, Deckard, Yang2018, CCFinder, and CloneWorks).

关键词： Clone Detection abstract syntax tree Fine-grained Splitting

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CloneBAS: A Code Clone Detection Method Based on abstract syntax tree and Simhash 3

CloneBAS: A Code Clone Detection Method Based on Abstract Sy...

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2023 IEEE 3rd International Conference on Data Science and Computer Application, ICDSCA 2023

作者： He, Hongkui Lin, Liannan Yu, Tianchen Zhong, Xinlei South China University of Technology Guangzhou China

ISBN: (纸本)9798350341546

Code clone, which is one of the common 'code smells', is the duplicate code that exists in the source code. In this paper, we present CloneBAS, a code clone detection method based on abstract syntax tree (AST) and Simhash. First, code block is considered as the detection granularity. Second, a fixed-length fingerprint with the AST of each code block is generated. As a result, multi-type clone detection capabilities are achieved. Third, based on the Hamming distance restriction, clone fingerprint groups and their corresponding clone code block groups are attained. Source codes from four projects are selected as subjects and the clone detectors of CCFinderSW and Deckard are compared with CloneBAS in our experiments. In terms of clone quality, the lowest accuracy of over 86% is achieved, which is better than the highest accuracy of 74% of the two comparators. In terms of clone quantity, Deckard is outperformed by CloneBAS in three projects. © 2023 IEEE.

关键词： abstract syntax tree code block fingerprint code clone detection Simhash

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Blockchain Based abstract syntax tree to Detect Vulnerability in IOT-Enabled Smart Contract 2

Blockchain Based Abstract Syntax Tree to Detect Vulnerabilit...

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2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023

作者： Mittal, Amit Widjaja, Gunawan Pecho, Renzon Daniel Cosme Kiruba, R. Roque, Jesus Marino Falcón Chandra, Alok Department of Allied Science Graphic Era Hill University Bhimtal263136 India Faculty of Law Universitas 17 Agustus 1945 Faculty of Public Health Universitas Indonesia Indonesia Lima Peru Sri Ramakrishna Engineering College Coimbatore641002 India Department of Environmental Engineering Universidad San Ignacio de Loyola La Molina Lima1524 Peru Lajpatrai institute of Management Mumbai Mumbai University India

ISBN: (纸本)9798350305418

As blockchain technology and the Internet of Things (IoT) have advanced, smart contracts have found new uses in many industries. Ethereum has become the go-to system for smart contract development. However, major financial losses have come from the ubiquity of smart contract vulnerabilities. Existing techniques have struggled to make efficient use of the syntax information in contract source code, making the detection of these vulnerabilities a key field of research. The reentrancy flaw in smart contracts is addressed by proposing a new vulnerability detection tool called SC-Defender . The abstract syntax tree representation of the source code for a smart contract written in the Solidity language is the core focus of SC-Defender 's deep learning technique. Its efficient vulnerability identification is the result of its use of a tree-based convolutional neural network. In order to remove superfluous branches while keeping the essential ones, we develop a pruning technique for abstract syntax trees. The vulnerability detection findings achieved by SC-Defender are encouraging, with a precision of 81.43 percent, a recall of 92.12 percent, and an F1-score of 86.4 five percent. The results of the abolition experiments show that the abstract syntax tree pruning technique is a crucial part of SC-Defender 's ability to find vulnerabilities. These improvements in smart contract vulnerability detection, especially in the context of IoT-enabled smart contracts, have a lot of potential to make smart contracts safer and reduce the economic risks that come with their broad use. © 2023 IEEE.

关键词： abstract syntax tree Blockchain convolutional neural network IOT Smart Contracts vulnerability detection

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