- author = "Christopher Salls and Aravind Machiry and Adam Doupe and Yan Shoshitaishvili and Christopher Kruegel and Giovanni Vigna",
- title = "Exploring Abstraction Functions in Fuzzing",
- booktitle = "2020 IEEE Conference on Communications and Network Security (CNS)",
- year = "2020",
- month = "29 " # jun # "-1 " # jul,
- address = "Avignon, France",
- keywords = "genetic algorithms, genetic programming, AFL, Fuzzing, Security, Computer bugs, Software, Conferences, Communication networks",
- isbn13 = "978-1-7281-4761-1",
-
DOI = "
doi:10.1109/CNS48642.2020.9162273",
- size = "9 pages",
-
abstract = "Fuzz testing has emerged as the preeminent automated
security analysis technique in the real world. To keep
up with the shifting security landscape, researchers
have innovated the fuzzing process to identify more and
more complex vulnerabilities. One innovation is an
approach inspired by genetic programming: the fuzzer
generates test-cases, evaluates the quality of the
test-case, and uses this evaluation to select
test-cases for further iterations of the process. While
this innovation has impressive results: without a
formal, scientific model on which to base these
improvements, the field of fuzzing has been explored in
an ad hoc way. As a result, it is difficult to
understand the relative merit of different
techniques.
we formalize the input evaluation and selection components of fuzzing, borrowing concepts from the field of static analysis, and providing a base for future expansion of and research into fuzzing techniques. In building this formalism, we observed that the impact of different abstraction functions in modern fuzzing techniques is under-explored in prior research. Without a formal base on which to reason about their contributions, researchers of fuzzing techniques have missed the potential for improvements to this critical component of fuzzing approaches. We explore the implications of our formalization-derived observation on the effectiveness of evolutionary fuzzing techniques in the second half of the paper, showing that the application of different abstraction functions, and the use of multiple abstraction functions in tandem, improves state-of-the-art fuzzing techniques.",
-
notes = "Also known as \cite{9162273}
University of California, Santa Barbara, USA",
