VLDB 2026 Research / reviewers in the wild / expert
Jiaxing Cheng
dblp:75/6439
· DBLP profile ↗
8ranked-venue papers
2as first author
6since 2021 · last 2026
0000-0001-8354-6452ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Computer networks · 3 · 1 first-author · 3 since 2021Security and privacy · 3 · 1 first-author · 3 since 2021Artificial intelligence and machine learning · 1Databases, data management, data science and information retrieval · 1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | Automated Construction of High-Quality Initial Seed Corpus for Network Protocol Fuzzing
Weicheng Lin, Laile Xi, Yaowen Zheng, Shenghao Lin, Jiaxing Cheng, Zhen Wang 0043, Shizhao Tian, Tianheng Qu, Hongsong Zhu |
INFOCOM | 5 |
| 2026 | An LLM-Guided Fuzzing of Proprietary Industrial Communication Protocols with Context Knowledge
Tianci Pan, Huan Qian, Yaowen Zheng, Haining Wang 0001, Peng Zhang 0044, Jiaxing Cheng, Ge Chu, Ke Li 0042, Ming Zhou 0010 |
INFOCOM | 6 |
| 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 | 1 |
| 2026 | ADGFUZZ: Assignment Dependency-Guided Fuzzing for Robotic Vehicles
Yaowen Zheng, Puzhuo Liu, Dongliang Fang, Jiaxing Cheng, Dingyi Shi, Limin Sun 0001 |
NDSS | 5 |
| 2025 | Discovering PLC Web Application Vulnerabilities Impacting Physical Control Using LLM-Based Fuzzing
Jiaxing Cheng, Dongliang Fang, Zhongwei Gu, Shichao Lv, Shuaizong Si, Limin Sun 0001 |
WASA (1) | 1 |
| 2021 | NEDetector: Automatically extracting cybersecurity neologisms from hacker forums
Jiaxing Cheng, Cheng Huang 0003, Zhouguo Chen, Weina Niu |
J. Inf. Secur. Appl. | 2 |
| 2008 | Identification of Interface Residues Involved in Protein-Protein Interactions Using Naïve Bayes Classifier
Chishe Wang, Jiaxing Cheng, Shoubao Su, Dongzhe Xu |
ADMA | 2 |
| 2005 | A Novel Genetic Algorithm for HP Model Protein FoldingabstractDetermination of the native state of a protein from its amino acid sequence is the goal of protein folding simulations, with potential applications in gene therapy and drug design. To predict a global minimum (GM) structure of a given sequence is a difficult task. A genetic algorithm (GA) is an efficient approach to find lowest-energy conformation for HP lattice model. We have introduced some new operators (symmetric and cornerchange operators) to speed up the searching process and give the result more biology significance. The result shows these new operators improved the success of prediction, compared with standard GA for benchmark HP sequence up to 50 residues Jie Song 0010, Jiaxing Cheng, Junjun Mao |
PDCAT | 2 |