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检索条件"主题词=Data Flow Graphs"

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Enhancing Smart Contract Vulnerability Detection with Hybrid Pre-Trained Models on Source Code and Byte Code 6

Enhancing Smart Contract Vulnerability Detection with Hybrid...

引用

6th International Conference on Frontier Technologies of Information and Computer, ICFTIC 2024

作者： Yang, Yuhao Xu, Hongxing Yao, Shaowen School of Software Yunnan University Kunming China

ISBN: (纸本)9798331541750

Smart contracts have caused significant economic losses due to various vulnerabilities, highlighting the importance of their security. Traditional vulnerability detection methods often focus on a single aspect, either neglecting the semantic and structural information in the source code or overlooking critical execution changes in the bytecode. This can result in incomplete security assessments. To address this, we propose HCBERT (HybridCodeBERT), a pre-trained hybrid model for smart contract vulnerability detection. HCBERT efficiently ex- tracts features from both source code and bytecode by fusing their information and integrating attention mechanisms with graph-based structures such as data flow and control flow graphs. Additionally, leveraging advanced pre-training techniques, we enhance the model's ability to capture complex patterns. Experimental results show that HCBERT achieves detection accuracies of 89.57%, 82.91 %, 94.73%, and 85.79% for four key vulnerabilities: reentrancy, delegate call, timestamp dependency, and overflow/underflow, outperforming existing state-of-the-art methods. Ablation experiments further confirm the benefits of feature fusion and the integration of graph structures and pre-training techniques in improving detection performance. © 2024 IEEE.

关键词： data flow graphs

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An empirical study on the impact of graph representations for code vulnerability detection using graph learning 7

An empirical study on the impact of graph representations fo...

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7th International Conference on Multimedia Analysis and Pattern Recognition, MAPR 2024

作者： Gia Lac, Le Tran Cao Cuong, Nguyen Hoang Son, Ngo Duc Pham, Van-Hau Duy, Phan The University of Information Technology Information Security Laboratory Ho Chi Minh City Viet Nam Vietnam National University Ho Chi Minh City Viet Nam

ISBN: (纸本)9798350368437

The increasing complexity of software systems has necessitated more sophisticated security measures, particularly in the domain of vulnerability detection. Traditional machine learning (ML) and deep learning (DL) techniques often fall short when source code is treated merely as text, prompting a shift toward graph learning methods that leverage specific graph representations of code to enhance detection capabilities. These representations, including Abstract Syntax Tree (AST), Control flow Graph (CFG), data flow Graph (DFG), Program Dependence Graph (PDG), and Code Property Graph (CPG), encapsulate the structural and semantic intricacies of programming code, offering a robust framework for identifying vulnerabilities. Despite advances in this field, a comprehensive understanding of the impact that each graph representation has on the effectiveness of vulnerability detection is still lacking. Our paper introduces a general architecture for a graph-based vulnerability detection system and conducts empirical studies on two real-world datasets, BigVul and FUNDED. This research systematically assesses how variations in graph representations - AST, CFG, DFG, PDG, and CPG - affect the efficacy of software vulnerability detection, providing pivotal insights that could guide future research and enhance practical applications in cybersecurity. © 2024 IEEE.

关键词： data flow graphs

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Software Fault Detection Based on Program Static Analysis and Fault Tree 4

Software Fault Detection Based on Program Static Analysis an...

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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Li, Yan Wuhan Qingchuan University Hubei Wuhan China

ISBN: (纸本)9798350352931

In today's software systems, software failures may lead to serious catastrophic consequences. This paper proposes a comprehensive method based on program static analysis and fault trees to detect software defects. First, use program static analysis technology to analyze the source code syntax and semantics, extract control flow graphs (CFG) and data flow graphs (DFG), and build an accurate program model. Secondly, based on the functional requirements of the software, a fault tree model is established to represent the possible failure modes of the software in a hierarchical and structured manner. Then, the program model is combined with the fault tree model to use logical reasoning and path analysis to identify critical paths and failure points that may lead to system failure. Finally, tools are developed to test and verify actual software systems. The method based on program static analysis and fault tree is significantly better than the traditional dynamic testing method in fault detection, with an average coverage rate of 91.27%. The combination of program static analysis and fault tree methods has significant advantages in high-safety software fault detection. © 2024 IEEE.

关键词： data flow graphs

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Enhancing Incremental dataflow Analysis in an IDE 24

Enhancing Incremental Dataflow Analysis in an IDE

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24th IEEE International Conference on Software Quality, Reliability and Security Companion, QRS-C 2024

作者： Harianto, Koko Wang, Feng-Jian Abouzeid, Mahmoud M. National Yang Ming Chiao Tung University Eecs International Graduate Program Hsinchu Taiwan National Yang Ming Chiao Tung University Department of Computer Science Hsinchu Taiwan

ISBN: (纸本)9798350365658

Incremental dataflow analysis is a conventional technique adopted in syntax-directed editors, popularly used in Integrated Development Environments (IDEs). However, dataflow anomaly detection during program editing in IDEs remains a challenge due to the interaction with newer functionalities, such as AI-powered plugins like Copilot, which might insert and modify code lines directly. These interactions impact both the efficiency and effectiveness of dataflow anomaly detection processes. This paper introduces an enhanced incremental dataflow analysis approach in IDEs, focused on the analysis of dataflow within program structures. Our approach introduces a modified version of SP-tree, named SP-graph. This modification, involving the addition of supporting edges, facilitates data storage and enhances dataflow analysis incrementally. We also present a mechanism that streamlines dataflow anomaly detection within IDEs by identifying modifications that may impact dataflow, updating relevant data, and conducting immediate analysis. In addition, incremental concurrent anomaly detection in SP-graph shall be studied further. We demonstrate the effectiveness of our approach through a case study. © 2024 IEEE.

关键词： data flow graphs

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An Overview+Detail Layout for Visualizing Compound graphs

An Overview+Detail Layout for Visualizing Compound Graphs

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2024 IEEE Visualization and Visual Analytics Conference, VIS 2024

作者： Han, Chang Lieffers, Justin Morrison, Clayton Isaacs, Katherine E. The University of Utah United States The University of Arizona United States Lum Ai

ISBN: (纸本)9798350354850

Compound graphs are networks in which vertices can be grouped into larger subsets, with these subsets capable of further grouping, resulting in a nesting that can be many levels deep. In several applications, including biological workflows, chemical equations, and computational data flow analysis, these graphs often exhibit a tree-like nesting structure, where sibling clusters are disjoint. Common compound graph layouts prioritize the lowest level of the grouping, down to the individual ungrouped vertices, which can make the higher level grouped structures more difficult to discern, especially in deeply nested networks. Leveraging the additional structure of the tree-like nesting, we contribute an overview+detail layout for this class of compound graphs that preserves the saliency of the higher level network structure when groups are expanded to show internal nested structure. Our layout draws inner structures adjacent to their parents, using a modified tree layout to place substructures. We describe our algorithm and then present case studies demonstrating the layout's utility to a domain expert working on data flow analysis. Finally, we discuss network parameters and analysis situations in which our layout is well suited. © 2024 IEEE.

关键词： data flow graphs

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Practical Experiment of Predicting Cash flows with LSTM and Double-entry Bookkeeping data

Practical Experiment of Predicting Cash Flows with LSTM and ...

引用

2024 IEEE International Conference on Big data, Bigdata 2024

作者： Motai, Ryoki Mashiko, Sota Kamebuchi, Masato Watanabe, Sora Matsumoto, Ryo Okada, Yukihiko University of Tsukuba Graduate School of Science and Technology Tsukuba Japan Tax Accountant Corporation Nihon Keiei Toyonaka Japan Center for Artificial Intelligence Research University of Tsukuba Institute of Systems and Information Engineering Tsukuba Institute for Advanced Research Tsukuba Japan

ISBN: (纸本)9798350362480

Double-entry bookkeeping data (DBD) systematically record the daily transactions of an organization and are the foundation of accounting information. Typically, they are more detailed and larger in scale than financial statements and feature cross-references between accounts. These characteristics make them beneficial for management. However, studies on quantitative evaluations of their usefulness in organizational management are scarce. This study investigates whether features derived from DBD by leveraging their characteristics can enhance future cash-balance predictions, with the primary focus on enhancing the cash management of organizations. To achieve this, DBD obtained from seven medical clinics were represented as graphs using their cross-references, and the graphs were then mapped into vector features based on graph similarity. The experimental results revealed that a long short-term memory model incorporating features unique to DBD obtained higher predictive accuracy than a simple long short-term memory model that only relies on past cash-balance time-series data. Additionally, an auto regressive integrated moving average model was more suitable for some clinics than the long short-term memory model. This can be attributed to the specific business characteristics and structural changes over time within each clinic. The main contribution of this study is the proposal and demonstration of a method that utilizes DBD, which are stored within organizations but often underutilized, to enhance cash management, thereby illustrating their significance. Based on our findings, business intelligence that provides predictive information using DBD could be developed by management professionals and companies offering accounting services. © 2024 IEEE.

关键词： data flow graphs

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Graphflow: A Fast and Accurate Distributed Streaming Graph Computation Model 30

GraphFlow: A Fast and Accurate Distributed Streaming Graph C...

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30th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2024

作者： Liang, Zheheng Zheng, Yingying Bi, Sheng Yao, Chaosheng Wang, Jiayan Xu, Lijie Ji, Shuping Wang, Wei Duan, Shikai Information Center Guangdong Power Grid Limited Liability Company Guangzhou China Joint Laboratory on Cyberspace Security China Southern Power Grid Guangzhou China State Key Lab of Computer Science at Iscas China University of Cas Beijing China Guangzhou Power Supply Bureau Guangzhou China University of Toronto Toronto Canada Nanjing Institute of Software Technology China University of Cas Nanjing China Ant Group-Payment Business Group Hangzhou China

ISBN: (纸本)9798331515966

Streaming graph computation has been widely applied in many fields, e.g., social network analysis and online product recommendation. However, existing streaming graph computation approaches still present limitations on accuracy and efficiency. To improve the accuracy, some distributed systems use the sequential graph update method based on an incremental computation model. However, these systems cannot handle the dynamic graph update concurrently. The speculation-based parallel updating model can parallelize the graph computation, however, it is restricted due to ignoring the original messages when updating a graph. Streaming graph computation usually requires high accuracy and low latency. As such, it is challenging to utilize incremental computation while simultaneously supplying concurrent processing *** overcome these challenges, in this paper, we first analyze a number of classical graph algorithms and summarize three principles that graph algorithms should satisfy in streaming scenarios. Based on these principles, we propose Graphflow, a streaming graph computation model. Graphflow achieves fast and accurate computation by utilizing incremental state update and propagation. To reduce the impact of concurrent update conflicts, Graphflow provides a fine-grained lock based parallel update strategy. We implement Graphflow framework and evaluate its performance and concurrent update conflict probability on real-world datasets. Meanwhile, we compare Graphflow with two existing representative graph processing systems. Experimental results show Graphflow achieves low latency and outperforms other graph processing systems given large datasets. © 2024 IEEE.

关键词： data flow graphs

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An automated construction method for symmetric encryption round function models based on dynamic analysis 9

An automated construction method for symmetric encryption ro...

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9th IEEE International Conference on data Science in Cyberspace, DSC 2024

作者： Du, Gaolei Sun, Yunge Qu, Junpeng Fu, Yong Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan250014 China Quan Cheng Laboratory Jinan250103 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan China

ISBN: (纸本)9798350391367

With the deepening of informatization, the incidence of cybercrimes utilizing malicious software for attacks is on the rise. This paper addresses the detection issue of symmetric encryption in malicious software from the perspective of cryptography, proposing an automated method for constructing a symmetric encryption round function model based on dynamic analysis. This method tracks data flow at the granularity of registers to characterize Directed Acyclic Graph (DAG) construction of round function models. Initially, dynamic execution traces of binary files are captured using Intel Pin, identifying loops within them. data flow tracking at the register level within loops is performed, creating DAG, and analyzing whether the loops exhibit characteristics of symmetric cipher round functions, thus constructing the round function model. This approach enables the detection of symmetric encryption within binary files and the construction of round function models, offering a novel solution for fine-grained symmetric cipher recognition and laying a foundation for detecting malicious software containing symmetric encryption. © 2024 IEEE.

关键词： data flow graphs

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A CRCLA Task Partition Algorithm Based on Ant Colony Algorithm 3

A CRCLA Task Partition Algorithm Based on Ant Colony Algorit...

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3rd International Symposium on Computer Engineering and Intelligent Communications, ISCEIC 2022

作者： Yang, Chen-Guang Wei, Li Tao, Chen Nan, Long-Mei Key Laboratory of Information Security Information Engineering University Zhengzhou450001 China

ISBN: (纸本)9781510660298

In order to find the optimal solution of task partitioning for data flow graph of cryptography algorithm based on reconfigurable logical array, a task partition algorithm ACP based on ant colony algorithm is *** algorithm first improves the cluster partition algorithm according to the hardware structure of reconfigurable array. On the basis of the ant colony algorithm, many ants in the ant colony algorithm are used to traverse the nodes in the data flow graph, and the number of divided blocks and the cost of inter-cluster communication are used as cost functions to continuously optimize the initialized pheromone concentration matrix and improve the algorithm. The experimental results show that the algorithm has the ability to obtain the optimal solution more quickly and stably than the CBP algorithm and the LSCBP algorithm. © The Authors. Published under a Creative Commons Attribution CC-BY 3.0 License.

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Traffic flow Prediction Method Based on Fast Statistics of Traffic flow and Graph Convolutional Network 8

Traffic Flow Prediction Method Based on Fast Statistics of T...

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8th IEEE International Conference on Intelligent Transportation Engineering, ICITE 2023

作者： Jiang, Dan Hou, Qun Liu, Xin Gao, Shidi Lingjiu HI-TECH Co. Ltd Hubei Wuhan China School of Artificial Intelligence Jianghan University Hubei Wuhan China Lingjiu HI-TECH Co. Ltd Hubei Wuhan China

ISBN: (纸本)9798350314212

Traffic flow prediction is an important issue in the intelligent transportation system, and traffic flow statistics is the data source of traffic flow prediction. The commonly used target detection and tracking traffic flow statistics methods are vulnerable to the impact of light, background, etc. in the actual scene, resulting in the problem of target loss and inaccurate tracking results. This article proposes a fast statistical method for vehicle flow, which uses trigger sensor devices in the road network to obtain vehicle images. In order to solve the problem of sensor counting errors caused by fast vehicle speeds and reduce the amount of data processed subsequently, the obtained vehicle images are first classified into target categories, with a target number greater than one and a target number equal to one. Then, target detection is performed on images with a target number greater than one, Finally, use statistical formulas for traffic flow statistics. Experiments have shown that using this method can reduce detection data by 30.8%, greatly improving detection efficiency. On this basis, this paper further uses the graph convolutional network(GCN) combined with the moving horizon to predict the traffic flow, analyzes the relationship between the topological structure of the traffic network and the time series data, effectively captures the time-space characteristics and evolution mode of the traffic flow, and establishes the latest characteristic mode of the traffic flow. This article uses a total of 16992 data points from the public datasets PeMS04 from January to February 2018 for training and testing. The experimental results showed that the root mean square error(RMSE) and mean absolute error(MAE) of the graph convolutional network traffic flow prediction model combined with the moving horizon decreased by 2.1 % on average and 2.3% on average. © 2023 IEEE.

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