.png){kind=icon}
.png){kind=icon}
Return
DeepFuzzer: Accelerated Deep Greybox Fuzzing
.png){kind=icon}
OA
AI
DOI：10.1109/TDSC.2019.2961339.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Fuzzing is one of the most effective vulnerability detection techniques, widely used in practice. However, the performance of fuzzers may be limited by their inability to pass complicated checks, inappropriate mutation frequency, arbitrary mutation strategy, or the variability of the environment. In this article, we present DeepFuzzer, an enhanced greybox fuzzer with qualified seed generation, balanced seed selection, and hybrid seed mutation. First, we use symbolic execution in a lightweight approach to generate qualified initial seeds which then guide the fuzzer through complex checks. Second, we apply a statistical seed selection algorithm to balance the mutation frequency between different seeds. Further, we develop a hybrid mutation strategy. The random and restricted mutation strategies are combined to maintain a dynamic balance between global exploration and deep search. We evaluate DeepFuzzer on the widely used benchmark Google fuzzer-test-suite which consists of real-world programs. Compared with AFL, AFLFast, FairFuzz, QSYM, and MOPT in the 24-hour experiment, DeepFuzzer discovers 30, 240, 102, 147, and 257 percent more unique crashes, executes 40, 36, 36, 98, and 15 percent more paths, and covers 37, 34, 34, 101, and 11 percent more branches, respectively. Furthermore, we present the practice of fuzzing a message middleware from Huawei with DeepFuzzer, and nine new vulnerabilities are reported.
Keywords:
Fuzzing
Middleware
Internet
Complexity theory
Task analysis
Space exploration
Instruments
Software testing
greybox fuzzing
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
7.5
Papers:
2.4K
Citations:
9.6K