EDBT 2026 Demo / reviewers in the wild / expert
Miaohua Li
dblp:311/8847
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2021
—ORCID · unresolved
Domains — the database's venue-derived domains; a paper can count in several
Software 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
1 paper |
Systems and software security · 100% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Systems and software security › vulnerability discovery › fuzzing
binary-only fuzzing |
0.5 | 1 | 2021 | BIFF: Practical Binary Fuzzing Framework for Programs of IoT and Mobile Devices · ASE 2021 |
Systems and software security › vulnerability discovery
fuzzing |
0.5 | 1 | 2021 | BIFF: Practical Binary Fuzzing Framework for Programs of IoT and Mobile Devices · ASE 2021 |
Systems and software security › vulnerability discovery › fuzzing
greybox fuzzing |
0.5 | 1 | 2021 | BIFF: Practical Binary Fuzzing Framework for Programs of IoT and Mobile Devices · ASE 2021 |
Systems and software security
vulnerability discovery |
0.5 | 1 | 2021 | BIFF: Practical Binary Fuzzing Framework for Programs of IoT and Mobile Devices · ASE 2021 |
Methods — techniques the papers use, named apart from their topics
coverage-guided fuzzing · 0.5binary instrumentation · 0.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2021 | BIFF: Practical Binary Fuzzing Framework for Programs of IoT and Mobile DevicesabstractInternet-of-things (IoT) or mobile devices are omnipresent in our daily life; the security issues inside them are especially crucial. Greybox fuzzing has been shown effective in detecting vulnerabilities. However, applications in IoT or mobile devices are usually proprietary to specific vendors, fuzzers are required to support binary-only targets. Moreover, since these devices are of heterogeneous architecture, assigned with limited resources, and many testing targets are server-like programs, applying existing fuzzing techniques faces great challenges.This paper proposes BIFF, a general-purpose fuzzer that aims to stress these issues. It supports binary-only targets, is general (supports multiple CPU architectures including Intel, ARM, MIPS, and PowerPC), fast (has the lowest runtime overhead compared to existing fuzzers), and flexible (uses a new fuzzing workflow that can fuzz any piece of code inside the target binary). Experiments demonstrate that BIFF has the best performance compared with state-of-the-art binary fuzzers and can fuzz the server-like programs which cannot be fuzzed by the existing fuzzers. Using BIFF, we’ve found 24 unknown vulnerabilities (including memory corruptions, infinite loops, and infinite recursions) in industrial products. Cen Zhang, Yuekang Li, Hongxu Chen 0001, Xiaoxing Luo, Miaohua Li, Anh Quynh Nguyen, Yang Liu 0003 |
ASE | 5 |