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Zian Liu

dblp:251/5438 · DBLP profile ↗
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4ranked-venue papers
3as first author
4since 2021 · last 2026
0000-0003-4803-0906ORCID · corroborated

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

Systems, architecture and hardware · 1 · 1 first-author · 1 since 2021Security and privacy · 1 · 1 first-author · 1 since 2021Software engineering, systems software and programming languages · 1 · 1 first-author · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 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.

Software engineering, system software, and programming languages
1 paper
Program analysis · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Program analysis
binary analysis
0.512021
Binary Code Similarity Detection · ASE 2021
Program analysis › binary analysis
binary code similarity detection
0.512021
Binary Code Similarity Detection · ASE 2021
Program analysis › program representation
graph-based code representation
0.512021
Binary Code Similarity Detection · ASE 2021
Program analysis
symbolic execution
0.512021
Binary Code Similarity Detection · ASE 2021

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

symbolic execution · 0.5graph similarity comparison · 0.5
YearPublicationVenuePosition
2026 FAPS-MER: Facial action position and semantic based interactive fusion for micro-expression recognition
Gang Yan 0001, Yubo He, Zian Liu, Yuqiang Guo, Shixin Cen
Signal Process. Image Commun.3
2024 VulMatch: Binary-Level Vulnerability Detection Through Signature
Zian Liu, Shigang Liu, Lei Pan 0002, Chao Chen 0015, Jun Zhang 0010, Dongxi Liu
NSS1
2022 Automated Binary Analysis: A Survey
Zian Liu, Chao Chen 0015, Dongxi Liu, Jun Zhang 0010
ICA3PP1
2021 Binary Code Similarity Detection
abstract
Binary code similarity detection is to detect the similarity of code at binary (assembly) level without source code. Existing work still has their limitation when dealing with mutated binary code with different compiling options. We proposed a novel approach to address this problem. By inspecting the binary code, we found that generally, within a function, some instructions aim to calculate (prepare) value for some other instructions. The latter instructions are defined by us the key instructions. Currently, we define four categories of key instructions: calling subfunctions, comparing instruction, returning instruction, and memory address writing instruction. Thus if we symbolically execute similar binary codes, the symbolic value at these key instructions should be similar. We implemented our idea into a tool. This prototype tool can: 1. symbolically execute binary code, 2. extract symbolic values at key instructions into a graph, and 3. compare the symbolic graph similarity. In our implementation, we also address some problems, including path explosion and loop handling.
Zian Liu
ASE1