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318 results on '"Sui, Yulei"'

56. A Smart Status Based Monitoring Algorithm for the Dynamic Analysis of Memory Safety.

Author

Chen, Zhe, Yan, Rui, Ma, Yingzi, Sui, Yulei, and Xue, Jingling

Subjects
MEMORY, ALGORITHMS, PROGRAMMING languages, DOMINANT language, COMPUTER software quality control
Abstract

C is a dominant programming language for implementing system and low-level embedded software. Unfortunately, the unsafe nature of its low-level control of memory often leads to memory errors. Dynamic analysis has been widely used to detect memory errors at runtime. However, existing monitoring algorithms for dynamic analysis are not yet satisfactory, as they cannot deterministically and completely detect some types of errors, such as segment confusion errors, sub-object overflows, use-after-frees and memory leaks. We propose a new monitoring algorithm, namely Smatus, short for smart status, that improves memory safety by performing comprehensive dynamic analysis. The key innovation is to maintain at runtime a small status node for each memory object. A status node records the status value and reference count of an object, where the status value denotes the liveness and segment type of this object, and the reference count tracks the number of pointer variables pointing to this object. Smatus maintains at runtime a pointer metadata for each pointer variable, to record not only the base and bound of a pointer's referent but also the address of the referent's status node. All the pointers pointing to the same referent share the same status node in their pointer metadata. A status node is smart in the sense that it is automatically deleted when it becomes useless (indicated by its reference count reaching zero). To the best of our knowledge, Smatus represents the most comprehensive approach of its kind. We have evaluated Smatus by using a large set of programs including the NIST Software Assurance Reference Dataset, MSBench, MiBench, SPEC and stress testing benchmarks. In terms of effectiveness (detecting different types of memory errors), Smatus outperforms state-of-the-art tools, Google's AddressSanitizer, SoftBoundCETS and Valgrind, as it is capable of detecting more errors. In terms of performance (the time and memory overheads), Smatus outperforms SoftBoundCETS and Valgrind in terms of both lower time and memory overheads incurred, and is on par with AddressSanitizer in terms of the time and memory overhead tradeoff made (with much lower memory overheads incurred). [ABSTRACT FROM AUTHOR]

Published
2024
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57. Per-Dereference Verification of Temporal Heap Safety via Adaptive Context-Sensitive Analysis

Author

Yan, Hua, Chen, Shiping, Sui, Yulei, Zhang, Yueqian, Zou, Changwei, Xue, Jingling, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, and Chang, Bor-Yuh Evan, editor

Published
2019
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68. Live Path CFI Against Control Flow Hijacking Attacks

Author

Barbar, Mohamad, Sui, Yulei, Zhang, Hongyu, Chen, Shiping, Xue, Jingling, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Pandu Rangan, C., Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Weikum, Gerhard, Series Editor, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Susilo, Willy, editor, and Yang, Guomin, editor

Published
2018
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76. Incremental Analysis for Probabilistic Programs

Author

Zhang, Jieyuan, Sui, Yulei, Xue, Jingling, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, and Ranzato, Francesco, editor

Published
2017
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78. How About Bug-Triggering Paths? - Understanding and Characterizing Learning-Based Vulnerability Detectors

Author

Cheng, Xiao, Nie, Xu, Li, Ningke, Wang, Haoyu, Zheng, Zheng, and Sui, Yulei

Abstract

Machine learning and its promising branch deep learning have proven to be effective in a wide range of application domains. Recently, several efforts have shown success in applying deep learning techniques for automatic vulnerability discovery, as alternatives to traditional static bug detection. In principle, these learning-based approaches are built on top of classification models using supervised learning. Depending on the different granularities to detect vulnerabilities, these approaches rely on learning models which are typically trained with well-labeled source code to predict whether a program method, a program slice, or a particular code line contains a vulnerability or not. The effectiveness of these models is normally evaluated against conventional metrics including precision, recall and F1 score. In this paper, we show that despite yielding promising numbers, the above evaluation strategy can be insufficient and even misleading when evaluating the effectiveness of current learning-based approaches. This is because the underlying learning models only produce the classification results or report individual/isolated program statements, but are unable to pinpoint bug-triggering paths, which is an effective way for bug fixing and the main aim of static bug detection. Our key insight is that a program method or statement can only be stated as vulnerable in the context of a bug-triggering path. In this work, we systematically study the gap between recent learning-based approaches and conventional static bug detectors in terms of fine-grained metrics called BTP metrics using bug-triggering paths. We then characterize and compare the quality of the prediction results of existing learning-based detectors under different granularities. Finally, our comprehensive empirical study reveals several key issues and challenges in developing classification models to pinpoint bug-triggering paths and calls for more advanced learning-based bug detection techniques.

Published
2024
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79. Cross-Language Taint Analysis: Generating Caller-Sensitive Native Code Specification for Java

Author

Kan, Shuangxiang, Gao, Yuhao, Zhong, Zexin, and Sui, Yulei

Abstract

Cross-language programming is a common practice within the software development industry, offering developers a multitude of advantages such as expressiveness, interoperability, and cross-platform compatibility, for developing large-scale applications. As an important example, JNI (Java Native Interface) programming is widely used in diverse scenarios where Java interacts with code written in other programming languages, such as C or C++. Conventional static analysis based on a single programming language faces challenges when it comes to tracing the flow of values across multiple modules that are coded in different programming languages. In this paper, we introduce CSS, a new Caller-Sensitive Specification approach designed to enhance the static taint analysis of Java programs employing JNI to interface with C/C++ code. In contrast to conservative specifications, this approach takes into consideration the calling context of the invoked C/C++ functions (or cross-language context), resulting in more precise and concise specifications for the side effects of native code. Furthermore, CSS specifically enhances the capabilities of Java analyzers, enabling them to perform precise static taint analysis across language boundaries into native code. The experimental results show that CSS can accurately summarize value-flow information and enhance the ability of Java monolingual static analyzers for cross-language taint flow tracking.

Published
2024
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82. Artifact of 'Recursive State Machine Guided Graph Folding for Context-Free Language Reachability'

Author

Lei, Yuxiang, Sui, Yulei, Tan, Shin Hwei, and Zhang, Qirun

Subjects
CFL-reachability, recursive state machines, graph simplification
Abstract

This is the artifact of the paper"Recursive State Machine Guided Graph Folding for Context-Free Language Reachability" accepted to PLDI 2023. The artifactis packaged as a Docker image "gf.tar.gz", which is to reproduce the experiment results of the paper. Please see README.pdf for detailed usage of the artifact.

Published
2023
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89. Self-inferencing Reflection Resolution for Java

Author

Li, Yue, Tan, Tian, Sui, Yulei, Xue, Jingling, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Kobsa, Alfred, editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Weikum, Gerhard, editor, and Jones, Richard, editor

Published
2014
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90. Region-Based Selective Flow-Sensitive Pointer Analysis

Author

Ye, Sen, Sui, Yulei, Xue, Jingling, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Kobsa, Alfred, Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Müller-Olm, Markus, editor, and Seidl, Helmut, editor

Published
2014
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100. SPAS: Scalable Path-Sensitive Pointer Analysis on Full-Sparse SSA

Author

Sui, Yulei, Ye, Sen, Xue, Jingling, Yew, Pen-Chung, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, and Yang, Hongseok, editor

Published
2011
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