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Qinming Dai - IEEE Xplore Author Profile
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Qinming Dai

Publications
2
Citations
8
Publications by Year
20212023
Co-Authors:
Show All Co-Authors (17)
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Qinming Dai

Affiliation

Zhejiang University, Hangzhou, China

Publication Topics

Biography

Qinming Dai is currently working toward the master degree with the School of Cyber Science and Technology, Zhejiang University. His research interests include software vulnerability mining, software test, and reverse engineering.(Based on document published on 4 July 2022).
Publications
2
Citations
8
Publications by Year
20212023
Co-Authors:
Show All Co-Authors (17)
Author's Published Works

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A Comprehensive Study on ARM Disassembly Tools

Muhui Jiang;Qinming Dai;Wenlong Zhang;Rui Chang;Yajin Zhou;Xiapu Luo;Ruoyu Wang;Yang Liu;Kui Ren

IEEE Transactions on Software Engineering
Year: 2023 | Volume: 49, Issue: 4 | Journal Article |
Cited by: Papers (2)
Embedded devices are becoming ubiquitous, and ARM is becoming the dominant architecture for them. Meanwhile, there is a pressing need to perform security assessments for these devices. Due to different types of peripherals, emulating the software, i.e., firmware, of these devices in scale is challenging. Therefore, static analysis is still widely used. Existing works usually leverage off-the-shelf...Show More

A Comprehensive Study on ARM Disassembly Tools

Muhui Jiang;Qinming Dai;Wenlong Zhang;Rui Chang;Yajin Zhou;Xiapu Luo;Ruoyu Wang;Yang Liu;Kui Ren

Year: 2023 | Volume: 49, Issue: 4 | Journal Article |

IFIZZ: Deep-State and Efficient Fault-Scenario Generation to Test IoT Firmware

Peiyu Liu;Shouling Ji;Xuhong Zhang;Qinming Dai;Kangjie Lu;Lirong Fu;Wenzhi Chen;Peng Cheng;Wenhai Wang;Raheem Beyah

2021 36th IEEE/ACM International Conference on Automated Software Engineering (ASE)
Year: 2021 | Conference Paper |
Cited by: Papers (6)
IoT devices are abnormally prone to diverse errors due to harsh environments and limited computational capabilities. As a result, correct error handling is critical in IoT. Implementing correct error handling is non-trivial, thus requiring extensive testing such as fuzzing. However, existing fuzzing cannot effectively test IoT error-handling code. First, errors typically represent corner cases, th...Show More

IFIZZ: Deep-State and Efficient Fault-Scenario Generation to Test IoT Firmware

Peiyu Liu;Shouling Ji;Xuhong Zhang;Qinming Dai;Kangjie Lu;Lirong Fu;Wenzhi Chen;Peng Cheng;Wenhai Wang;Raheem Beyah

Year: 2021 | Conference Paper |

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