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检索条件"主题词=Data Flow Analysis"

共 1439 条记录，以下是91-100 订阅

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CWEInject: Build fuzzer benchmark through bug templates 3

CWEInject: Build fuzzer benchmark through bug templates

引用

3rd International Conference on Electronic Information Engineering and data Processing, EIEDP 2024

作者： Ma, Zongshuai Wu, Zehui Wei, Qiang School of Cyber Science and Engineering University of Information Engineering Zhengzhou China

ISBN: (纸本)9781510680531

With the development of fuzzy testing techniques and the generation of new techniques, evaluating and comparing the performance of fuzzy testers is a key challenge, and the evaluation index system needs to be supported by reliable test sets. Benchmark test sets with explicit, realistic vulnerabilities can be a good way of evaluating the ability of fuzzy testers to find in-the-wild vulnerabilities, and make the evaluation results have reliability. In order to batch a benchmark construction, this paper proposes CWEInject, a tool that performs data flow analysis on a target program, dynamically tracks the locations that satisfy the vulnerability template injection, and then injects the vulnerability template into the program to generate a vulnerability program. Our approach can generate vulnerability programs that satisfy the test benchmarks for fuzzy testing. Finally, we conducted experiments on six open-source programs, through which we demonstrated that the benchmark test set generated by the method is suitable for evaluating fuzzy testing tools as well as comparing the performance of different improved techniques. © 2024 SPIE.

关键词： data flow analysis

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DDLDroid: Efficiently Detecting data Loss Issues in Android Apps 2023

DDLDroid: Efficiently Detecting Data Loss Issues in Android ...

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

作者： Zhou, Yuhao Song, Wei Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing Peoples R China

ISBN: (纸本)9798400702211

data loss issues in Android apps triggered by activity restart or app relaunch significantly reduce the user experience and undermine the app quality. While data loss detection has received much attention, the state-of-the-art techniques still miss many data loss issues due to the inaccuracy of the static analysis or the low coverage of the dynamic exploration. To this end, we present DDLDroid, a static analysis approach and an open-source tool, to systematically and efficiently detect data loss issues based on the data flow analysis. DDLDroid is bootstrapped by a saving-restoring bipartite graph which correlates variables that need saving to the corresponding variables that need restoring according to their carrier widgets. The missed or broken saving or restoring data flows lead to data loss issues. The experimental evaluation on 66 Android apps demonstrates the effectiveness and efficiency of our approach: DDLDroid successfully detects 302 true data loss issues in 73 minutes, 180 of which are previously unknown.

关键词： Android apps data loss data flow analysis bug detection

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Building an Inference Server Platform for Large Language Models Using dataflow PIM Platform

Building an Inference Server Platform for Large Language Mod...

引用

2024 International Conference on Electronics, Information, and Communication, ICEIC 2024

作者： Choi, Kyu Hyun Hwang, Taeho SoC Platform Research Center Korea Electronics Technology Institute Korea Republic of

ISBN: (纸本)9798350371888

Processing-in-Memory (PIM) has garnered attention as a platform for large language model inference due to its ability to perform computations within memory, leveraging the internal bandwidth of memory components. In data center environments, to execute AI models across multiple nodes, an inference server is typically deployed at the data center's frontend. This server orchestrates the assignment of AI inference tasks to the appropriate nodes. This paper presents the construction of an open source-based inference server designed for easy deployment of a PIM platform grounded in data flow architecture within a data center setting. We have conducted operational tests on large language models to validate the efficacy of our approach. © 2024 IEEE.

关键词： data flow analysis

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SafeNet: Towards mitigating replaceable unsafe Rust code via a recommendation-based approach

SafeNet: Towards mitigating replaceable unsafe Rust code via...

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作者： Dong, Yan Zhang, Zhicong Cui, Mohan Xu, Hui School of Computer Science Fudan University Shanghai China

Rust is a system-level programming language with advantages in memory safety. It ensures that any Rust programs without unsafe code should not incur undefined behaviours. However, unsafe code still plays an essential role in Rust to achieve low-level control. Therefore, a major design pattern of Rust programs is interior unsafe, which wraps unsafe code as safe APIs and handles all undefined behaviours internally. Rust standard library already provides a rich set of safe APIs to facilitate Rust code development. Nevertheless, due to unfamiliarity with these APIs, developers may misuse unnecessary unsafe code and suffer memory-safety risks. In this paper, we investigate an approach to mitigate replaceable unsafe code. We first analyse unsafe APIs of the Rust standard library and summarize their common usage patterns. Each pattern corresponds to one or several code samples in our knowledge base. Then, we develop an approach to automatically recognize the usage pattern and recommend corresponding code samples. Our approach leverages dataflow analysis to exclude impossible patterns and employs a BERT-based machine learning model to find the most similar pattern among the rest. We have conducted evaluation experiments with 472 unsafe code snippets collected from GitHub projects and successfully recognized the pattern of 394 snippets. We hope our approach can assist developers in detecting unnecessary unsafe code and suggesting safe alternatives. © 2024 John Wiley & Sons Ltd.

关键词： data flow analysis

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Heterogeneous System Modeling Using Timed- and Untimed-Based Models of Computation: A Case Study for Avionics Systems Domain 43

Heterogeneous System Modeling Using Timed- and Untimed-Based...

引用

43rd AIAA DATC/IEEE Digital Avionics Systems Conference, DASC 2024

作者： Duarte, Gabriel C. Loubach, Denis S. Aeronautics Institute of Technology Ita Department of Computer Systems SP São José dos Campos Brazil

ISBN: (纸本)9798350349610

The main characteristic of avionics systems that makes it a unique application of embedded systems (or cyber-physical system - CPS) is its extremely safety-critical nature. By itself, this makes the design of such a system to be challenging. With the addition of new trends such as artificial intelligence (AI), the use of multicore processors, reconfigurable computing and many others, designing avionics systems is becoming even more complex. These trends allow new capabilities, but we also need to provide confidence to maintain the safety and security of avionics systems. One possible approach is a CPS design using formal models of computation (MoCs), such as the synchronous (SY) and synchronous dataflow (SDF). MoCs are mathematical rules that dictate how software or hardware computations are executed. With that approach, one can design a system at a high-level of abstraction and achieve a correct-by-construction implementation. Avionics systems are safety-critical due to their hard real-time requirements. That would require the designers to model avionics using a timed MoC, such as the SY. On the other hand, runtime reconfiguration can be modeled at a higher level of abstraction using MoCs that are based on the SDF, such as the scenario-aware dataflow (SADF). Therefore, that scenario requires a heterogeneous modeling mixing both timed and untimed MoCs, which is a gap we target in this paper. Here, we introduce the interfaces for communication between the SY and SDF MoCs in a functional programming paradigm framework, allowing heterogeneous system modeling mixing timed and untimed MoCs at a high-level of abstraction. © 2024 IEEE.

关键词： data flow analysis

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DaYu: Optimizing Distributed Scientific Workflows by Decoding dataflow Semantics and Dynamics

DaYu: Optimizing Distributed Scientific Workflows by Decodin...

引用

2024 IEEE International Conference on Cluster Computing, CLUSTER 2024

作者： Tang, Meng Cernuda, Jaime Ye, Jie Guo, Luanzheng Tallent, Nathan R. Kougkas, Anthony Sun, Xian-He Illinois Institute of Technology United States Pacific Northwest National Laboratory United States

ISBN: (纸本)9798350358711

The combination of ever-growing scientific datasets and distributed workflow complexity creates I/O performance bottlenecks due to data volume, velocity, and variety. Although the increasing use of descriptive data formats (e.g., HDF5, netCDF) helps organize these datasets, it also introduces obscure bottlenecks due to the need to translate high-level operations into file addresses and then into low-level I/O operations. To address this challenge, we introduce DaYu, a method and toolset for analyzing (a) semantic relationships between logical datasets and file addresses, (b) how dataset operations translate into I/O, and (c) the combination across entire workflows. DaYu's analysis and visualization enable the identification of critical bottlenecks and the reasoning about remediation. We describe our methodology and propose optimization guidelines. Evaluation on scientific workflows demonstrates up to a 3.7x performance improvement in I/O time for obscure bottlenecks. The time and storage overhead for DaYu's time-ordered data are typically under 0.2% of runtime and 0.25% of data volume, respectively. © 2024 IEEE.

关键词： data flow analysis

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Programming Fully Disaggregated Systems 23

Programming Fully Disaggregated Systems

引用

19th ACM Workshop on Hot Topics in Operating Systems (HotOS)

作者： Anneser, Christoph Vogel, Lukas Gruber, Ferdinand Bandle, Maximilian Giceva, Jana Tech Univ Munich Munich Germany

ISBN: (纸本)9798400701955

With full resource disaggregation on the horizon, it is unclear what the most suitable programming model is that enables dataflow developers to fully harvest the potential that recent hardware developments offer. In our vision, we propose to raise the abstraction level to allow developers to primarily reason about their dataflow and the requirements that need to be met by the underlying system in a declarative fashion. Underneath, the system works with typed memory regions and uses the notion of ownership that allows for more flexible memory management across the different compute devices and the tasks mapped onto them. This requires a holistic approach that crosses multiple layers of the system stack, opening exciting systems research questions.

关键词： data flow analysis

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A Plugin for Kotlin based Android Apps to Detect Security Breaches through dataflow 47

A Plugin for Kotlin based Android Apps to Detect Security Br...

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47th IEEE-Computer-Society Annual International Conference on Computers, Software, and Applications (COMPSAC)

作者： Talukder, Md Arabia Islam Mishu, Sumaiya Farzana Shahriar, Hossain Riad, A. B. M. Kamml Islam Wu, Fan Rahman, Akond Kennesaw State Univ Coll Comp & Software Engn Marietta GA 30060 USA Tuskegee Univ Dept Comp Sci Tuskegee AL 36088 USA Auburn Univ Dept Comp Sci & Software Engn Auburn AL 36849 USA

ISBN: (纸本)9798350326970

Android developers have already adopted Kotlin as their preferred language. Kotlin is more well-accepted than Java because of its simplicity, readability, and new features like scope functions, extension functions, null safety, etc. Google has introduced several new libraries such as RoomDb, Jetpack-Compose, as well as dependency injection frameworks like Dagger-Hilt to enhance Android Development. The question is how secure these libraries are? Are there tools available to analyze new Libraries? DroidPatrol [1] is our existing static analysis plugin that works on Android Apps written in Java. To support Kotlin code analysis we were continuously working on upgrading our plugin. Finally, we released DroidPatrol 2.0 [2] which is compatible to perform static analysis of Android apps developed in Kotlin. It also works on Java-based Android app developed in Java. In this latest edition, we restructured the architecture of the plugin to optimize its efficiency. We also found a vulnerability in RoomDb. The latest version is developed in Kotlin, and we used IntelliJ Idea. Lastly, version 2.0 is independent of Android Studio editions meaning that any version of Android Studio is compatible.

关键词： Android Software Security SQL-Injection RoomDb Dependency Injection Static analysis Kotlin Plugin data flow analysis Call graph Mobile Security

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Model-based Confidentiality analysis under Uncertainty 20

Model-based Confidentiality Analysis under Uncertainty

引用

IEEE 20th International Conference on Software Architecture (ICSA)

作者： Hahner, Sebastian Bitschi, Tizian Walter, Maximilian Bure, Tomas Hnetynka, Petr Heinrich, Robert Karlsruhe Inst Technol Karlsruhe Germany Charles Univ Prague Prague Czech Republic

ISBN: (纸本)9781665464598

In our connected world, ensuring the confidentiality of the software systems we build becomes increasingly difficult. Model-based design time confidentiality analyses have been proposed to cope with this complexity early. However, the usefulness of such analyses is limited due to uncertainty about the software architecture itself and the software's execution environment. This leads to conclusions about confidentiality violations that lack both precision and comprehensiveness. Although there exist approaches to deal with design time uncertainty, existing research lacks precise statements about the impact of uncertainty on confidentiality. To address this, we include uncertainty as part of our software architectural model. We extend a data flow-based analysis to include the impact of uncertainty on confidentiality violations. The results of the case study-based evaluation show high accuracy with typical design time uncertainty. Also, our analysis yields more precise statements about the impact of uncertainty on confidentiality than the state of the art.

关键词： Model-driven Security Software Architecture Uncertainty Confidentiality data flow analysis Access Control

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SORA: Rapid Software Pipelining Optimization after Register Allocation for Vector-DSPs 22

SORA: Rapid Software Pipelining Optimization after Register ...

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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Xie, Anxing Hu, Yonghua Li, Zhe Wang, Yaohua Hunan University of Science and Technology Xiangtan China National University of Defense Technology Changsha China

ISBN: (纸本)9798331509712

Heterogeneous multi-zone processors are key building blocks of high performance computing (HPC), and fully utilizing its hardware resources is essential to unlocking its full power. Software pipelining is a family of techniques that enhance program execution efficiency by accelerating the execution speed of loop programs through parallel execution of instructions from different loop bodies. However, existing software pipelining methods fail to account for the unique hardware architectures of digital signal processors (DSPs), resulting in suboptimal performance when applied *** this paper, we present SORA, a rapid Software pipelining Optimization architecture performing efficient instruction scheduling after Register Allocation for the Vector-DSPs. In addition, SORA conducts a comprehensive analysis encompassing instruction type, data flow, functional unit utilization, and register conflict, tailored to accommodate Vector-DSPs. Experimental results demonstrate that SORA can achieve 2.78∼24.1× speedup in the batch normalization, 2D convolution, relu and matrix multiplication, and 1.14× in the ResNet-18 neural network to the original program. Additionally, there is a 1.05∼6.59× improvement compared to loop unrolling optimization. © 2024 IEEE.

关键词： data flow analysis

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