![](/images/graphics-bg.png)
RDFuzz: Accelerating Directed Fuzzing with Intertwined Schedule and Optimized Mutation
Joint Authors
Source
Mathematical Problems in Engineering
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-03-17
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
Directed fuzzing is a practical technique, which concentrates its testing energy on the process toward the target code areas, while costing little on other unconcerned components.
It is a promising way to make better use of available resources, especially in testing large-scale programs.
However, by observing the state-of-the-art-directed fuzzing engine (AFLGo), we argue that there are two universal limitations, the balance problem between the exploration and the exploitation and the blindness in mutation toward the target code areas.
In this paper, we present a new prototype RDFuzz to address these two limitations.
In RDFuzz, we first introduce the frequency-guided strategy in the exploration and improve its accuracy by adopting the branch-level instead of the path-level frequency.
Then, we introduce the input-distance-based evaluation strategy in the exploitation stage and present an optimized mutation to distinguish and protect the distance sensitive input content.
Moreover, an intertwined testing schedule is leveraged to perform the exploration and exploitation in turn.
We test RDFuzz on 7 benchmarks, and the experimental results demonstrate that RDFuzz is skilled at driving the program toward the target code areas, and it is not easily stuck by the balance problem of the exploration and the exploitation.
American Psychological Association (APA)
Ye, Jiaxi& Li, Ruilin& Zhang, Bin. 2020. RDFuzz: Accelerating Directed Fuzzing with Intertwined Schedule and Optimized Mutation. Mathematical Problems in Engineering،Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1200694
Modern Language Association (MLA)
Ye, Jiaxi…[et al.]. RDFuzz: Accelerating Directed Fuzzing with Intertwined Schedule and Optimized Mutation. Mathematical Problems in Engineering No. 2020 (2020), pp.1-12.
https://search.emarefa.net/detail/BIM-1200694
American Medical Association (AMA)
Ye, Jiaxi& Li, Ruilin& Zhang, Bin. RDFuzz: Accelerating Directed Fuzzing with Intertwined Schedule and Optimized Mutation. Mathematical Problems in Engineering. 2020. Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1200694
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1200694