VLDB 2026 Research / reviewers in the wild / expert
Yibo Qu
dblp:412/6968
· DBLP profile ↗
2ranked-venue papers
0as first author
2since 2021 · last 2026
—ORCID · unresolved
Domains — the database's venue-derived domains; a paper can count in several
Security and privacy · 1 · 1 since 2021Software engineering, systems software and programming languages · 1 · 1 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.
| Network and information security
2 papers |
Systems and software security · 35% Cyber-physical and IoT security · 35% Network security · 30% | |
| Software engineering, system software, and programming languages
1 paper |
Program analysis · 100% |
Topics — the 5 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Cyber-physical and IoT security
industrial control system security |
1.0 | 1 | 2026 | An LLM-Driven Fuzzing Framework for Detecting Logic Instruction Bugs in PLCs · NDSS 2026 |
Systems and software security
vulnerability discovery |
1.0 | 1 | 2026 | An LLM-Driven Fuzzing Framework for Detecting Logic Instruction Bugs in PLCs · NDSS 2026 |
Network security
protocol reverse engineering |
0.9 | 1 | 2025 | Breaking the Traffic Barrier: Unveiling Multi-Format of Protocols via Autonomous Program Exploration · ASE 2025 |
Program analysis
dynamic analysis |
0.9 | 1 | 2025 | Breaking the Traffic Barrier: Unveiling Multi-Format of Protocols via Autonomous Program Exploration · ASE 2025 |
Program analysis › dynamic analysis
program tracing |
0.9 | 1 | 2025 | Breaking the Traffic Barrier: Unveiling Multi-Format of Protocols via Autonomous Program Exploration · ASE 2025 |
Methods — techniques the papers use, named apart from their topics
implicit data flow analysis · 1.7constraint extraction · 1.7constraint combination · 1.7autonomous program exploration · 1.7large language model · 1.0fuzzing · 1.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | An LLM-Driven Fuzzing Framework for Detecting Logic Instruction Bugs in PLCs
Jiaxing Cheng, Ming Zhou 0010, Haining Wang 0001, Xin Chen 0123, Yibo Qu, Limin Sun 0001 |
NDSS | 6 |
| 2025 | Breaking the Traffic Barrier: Unveiling Multi-Format of Protocols via Autonomous Program ExplorationabstractProtocol reverse engineering (PRE) aims to infer the protocol formats of unknown protocols. Existing techniques, whether Network-Trace based or Execution-Trace based methods, face two main limitations: a reliance on the quality and scale of traffic datasets, which often leads to low accuracy and poor generalization; and a failure to adequately consider the multi-format characteristic prevalent in real-world protocols (i.e., the same protocol may support multiple different formats).To address these challenges, we propose ProbePRE—a PRE tool that performs multi-format extraction on protocol handlers by autonomously generating packets. ProbePRE employs three key techniques: (1) an execution tracing strategy enhanced with implicit data flow analysis to obtain more detailed execution information; (2) constraint extraction methods tailored for different program structures to pass protocol validation; and (3) an innovative constraint combination algorithm to construct effective packets that guide the protocol handler to execute diverse protocol parsing paths. In our experimental evaluation, we compared ProbePRE with 4 state-of-the-art PRE tools in terms of field segmentation accuracy. The results demonstrated that ProbePRE achieved an F1 score of 0.88, significantly outperforming existing methods. Furthermore, evaluations on 6 protocol handlers indicated that ProbePRE attained 83% completeness in multi-format extraction tasks. Notably, in basic block coverage tests, ProbePRE achieved a 67% improvement over traditional traffic dataset methods, which fully validates the effectiveness of its path exploration capabilities. Dingzhao Xue, Yibo Qu, Xin Chen 0123, Shuaizong Si, Shichao Lv, Zhiqiang Shi, Limin Sun 0001 |
ASE | 2 |