Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Haiqing Qiu

dblp:361/2111 · DBLP profile ↗
← Back
3ranked-venue papers
0as first author
3since 2021 · last 2025
0009-0001-9978-249XORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Security and privacy · 3 · 3 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Software engineering, system software, and programming languages
1 paper
Program analysis · 77% Operating systems · 23%
Network and information security
1 paper
Systems and software security · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Systems and software security
vulnerability discovery
0.712023
KRover: A Symbolic Execution Engine for Dynamic Kernel Analysis · CCS 2023
Program analysis
symbolic execution
0.712023
KRover: A Symbolic Execution Engine for Dynamic Kernel Analysis · CCS 2023
Operating systems › system security › operating system security
kernel security
0.212023
KRover: A Symbolic Execution Engine for Dynamic Kernel Analysis · CCS 2023

Methods — techniques the papers use, named apart from their topics

symbolic execution · 1.3dynamic analysis · 1.3
YearPublicationVenuePosition
2025 PRISM: To Fortify Widget Based User-App Data Exchanges Using Android Virtualization Framework
YingTat Ng, Haiqing Qiu, Xuhua Ding
AsiaCCS3
2023 KRover: A Symbolic Execution Engine for Dynamic Kernel Analysis
abstract
We present KRover, a novel kernel symbolic execution engine catered for dynamic kernel analysis such as vulnerability analysis and exploit generation. Different from existing symbolic execution engines, KRover operates directly upon a live kernel thread's virtual memory and weaves symbolic execution into the target's native executions. KRover is compact as it neither lifts the target binary to an intermediary representation nor uses QEMU or dynamic binary translation. Benchmarked against S2E, our performance experiments show that KRover is up to 50 times faster but with one tenth to one quarter of S2E memory cost. As shown in our four case studies, KRover is noise free, has the best-possible binary intimacy and does not require prior kernel instrumentation. Moreover, a user can develop her kernel analyzer that not only uses KRover as a symbolic execution library but also preserves its independent capabilities of reading/writing/controlling the target runtime. Namely, the resulting analyzer on top of KRover integrates symbolic reasoning and conventional dynamic analysis and reaps the benefits of their reinforcement to each other.
Pansilu Pitigalaarachchi, Xuhua Ding, Haiqing Qiu, Haoxin Tu, Jiaqi Hong, Lingxiao Jiang
CCS3
2023 DScope: To Reliably and Securely Acquire Live Data from Kernel-Compromised ARM Devices
Haiqing Qiu, Xuhua Ding
ESORICS (4)2