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22 results on '"Du, Yunlan"'

1. An Empirical Study on Benchmarks of Artificial Software Vulnerabilities

Author

Geng, Sijia, Li, Yuekang, Du, Yunlan, Xu, Jun, Liu, Yang, and Mao, Bing

Subjects
Computer Science - Cryptography and Security
Abstract

Recently, various techniques (e.g., fuzzing) have been developed for vulnerability detection. To evaluate those techniques, the community has been developing benchmarks of artificial vulnerabilities because of a shortage of ground-truth. However, people have concerns that such vulnerabilities cannot represent reality and may lead to unreliable and misleading results. Unfortunately, there lacks research on handling such concerns. In this work, to understand how close these benchmarks mirror reality, we perform an empirical study on three artificial vulnerability benchmarks - LAVA-M, Rode0day and CGC (2669 bugs) and various real-world memory-corruption vulnerabilities (80 CVEs). Furthermore, we propose a model to depict the properties of memory-corruption vulnerabilities. Following this model, we conduct intensive experiments and data analyses. Our analytic results reveal that while artificial benchmarks attempt to approach the real world, they still significantly differ from reality. Based on the findings, we propose a set of strategies to improve the quality of artificial benchmarks.

Published
2020

3. HART: Hardware-Assisted Kernel Module Tracing on Arm

Author

Du, Yunlan, Ning, Zhenyu, Xu, Jun, Wang, Zhilong, Lin, Yueh-Hsun, Zhang, Fengwei, Xing, Xinyu, Mao, Bing, 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, Chen, Liqun, editor, Li, Ninghui, editor, Liang, Kaitai, editor, and Schneider, Steve, editor

Published
2020
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4. Density Peak Clustering Algorithm Based on Differential Privacy Preserving

Author

Chen, Yun, Du, Yunlan, Cao, Xiaomei, 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, Liu, Feng, editor, Xu, Jia, editor, and Xu, Shouhuai, editor

Published
2019
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11. Additional file 1 of Patients with Parkinson’s disease predict a lower incidence of colorectal cancer

Author

Fang, Hongsheng, Du, Yunlan, Pan, Shuting, Zhong, Ming, and Tang, Jiayin

Abstract

Additional file 1: Fig. S1 Forest plot of subgroup analysis (colon). Fig. S2 Forest plot of subgroup analysis (rectum). Fig. S3 Forest plot of subgroup analysis (America). Fig. S4 Forest plot of subgroup analysis (Europe). Fig. S5 Forest plot of subgroup analysis (Asia). Fig. S6 Sensitivity analysis of cohort study. Fig. S7 Funnel plot of cohort study. Fig. S8 Egger’s tests of cohort study.

Published
2021
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15. Mapping to Bits

Author

Pang, Chengbin, primary, Du, Yunlan, additional, Mao, Bing, additional, and Guo, Shanqing, additional

Published
2018
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16. POMP++: Facilitating Postmortem Program Diagnosis with Value-Set Analysis

Author

Mu, Dongliang, Du, Yunlan, Xu, Jianhao, Xu, Jun, Xing, Xinyu, Mao, Bing, and Liu, Peng

Abstract

With the emergence of hardware-assisted processor tracing, execution traces can be logged with lower runtime overhead and integrated into the core dump. In comparison with an ordinary core dump, such a new post-crash artifact provides software developers and security analysts with more clues to a program crash. However, existing works only rely on the resolved runtime information, which leads to the limitation in data flow recovery within long execution traces. In this work, we propose POMP++, an automated tool to facilitate the analysis of post-crash artifacts. More specifically, POMP++ introduces a reverse execution mechanism to construct the data flow that a program followed prior to its crash. Furthermore, POMP++ utilizes Value-set Analysis, which helps to verify memory alias relation, to improve the ability of data flow recovery. With the restored data flow, POMP++ then performs backward taint analysis and highlights program statements that actually contribute to the crash. We have implemented POMP++ for Linux system on x86-32 platform, and tested it against various crashes resulting from 31 distinct real-world security vulnerabilities. The evaluation shows that, our work can pinpoint the root causes in 29 cases, increase the number of recovered memory addresses by 12 percent and reduce the execution time by 60 percent compared with existing reverse execution. In short, POMP++ can accurately and efficiently pinpoint program statements that truly contribute to the crashes, making failure diagnosis significantly convenient.

Published
2021
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22. Adeno-Associated Virus Type 2 Vector-Mediated Glial Cell Line-Derived Neurotrophic Factor Gene Transfer Induces Neuroprotection and Neuroregeneration in a Ubiquitin-Proteasome System Impairment Animal Model of Parkinson's Disease.

Author

Du, Yunlan, Zhang, Xiaojie, Tao, Qingqing, Chen, Sheng, and Le, Weidong

Subjects
*ADENO-associated virus, *GLIAL cell line-derived neurotrophic factor, *GENETIC transformation, *NEURODEGENERATION, *CENTRAL nervous system diseases, *THERAPEUTICS, *PROTEASOME inhibitors, *UBIQUITIN, *PARKINSON'S disease, *ANIMAL models in research, *PROTEASOMES
Abstract

Background: The impairment of the ubiquitin-proteasome system (UPS) is a cellular mechanism underlying the neurodegenerative process in Parkinson's disease (PD). A mouse model induced by the selective proteasome inhibitor lactacystin targeting on substantia nigra has been demonstrated to be valuable in investigating etiopathogenesis and neuroprotection for PD. Objective: In the present study, we used adeno-associated virus type 2 vector (AAV2) encoding glial cell line-derived neurotrophic factor (GDNF) injected into the striatum of this animal model to test the effectiveness and possible mechanisms of GDNF gene therapy. Results: Our results showed that AAV2-mediated GDNF gene therapy significantly attenuated lactacystin-induced loss of nigral dopamine (DA) neurons and striatal DA levels. Furthermore, we found that GDNF protein is mostly expressed in astrocytes in the subventricular zone (SVZ) and dentate gyrus (DG). AAV2-mediated GDNF therapy can induce neurogenesis in the SVZ and DG, and increase the number of nigral newborn DA neurons. Conclusion: These data indicate that AAV2-mediated GDNF gene therapy can protect the nigral DA neurons from the UPS impairment-induced degeneration, which may partly result from the nigral DA neuron regeneration in the brain, and such experimental results may have implications for the treatment of PD. Copyright © 2012 S. Karger AG, Basel [ABSTRACT FROM AUTHOR]

Published
2013
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