Optimization Research of Directed Fuzzing Based on AFL
Open Access
- 7 December 2022
- journal article
- research article
- Published by MDPI AG in Electronics
- Vol. 11 (24), 4066
- https://doi.org/10.3390/electronics11244066
Abstract
Fuzz testing is the process of testing programs by continually producing unique inputs in order to detect and identify security flaws. It is often used in vulnerability mining. The most prevalent fuzzing approach is grey-box fuzzing, which combines lightweight code instrumentation with data-feedback-driven generation of fresh program input seeds. AFL (American Fuzzy Lop) is an outstanding grey-box fuzzing tool that is well known for its quick fork server execution, dependable genetic algorithm, and numerous mutation techniques. AFLGO proposes and executes power scheduling based on a simulated annealing process for a more appropriate energy allocation to seeds, however it is neither reliable nor successful. To tackle this issue, we offer an energy-dynamic scheduling strategy based on the algorithm of the fruit fly. Adjusting the energy of the seeds dynamically controls the production of test cases. The findings demonstrate that the approach suggested in this research can test the target region more rapidly and thoroughly and has a high application value for patch testing and vulnerability replication.Keywords
Funding Information
- National Natural Science Foundation of China (62162039, 61762060)
This publication has 19 references indexed in Scilit:
- Comparative Study Of Various Approaches Of Dijkstra AlgorithmPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2021
- Probabilistic Path Prioritization for Hybrid FuzzingIEEE Transactions on Dependable and Secure Computing, 2020
- Research on Network Attack and Defense Based on Artificial Intelligence TechnologyPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2020
- Web Application Vulnerability Fuzzing Based On Improved Genetic AlgorithmPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2020
- Full-Speed Fuzzing: Reducing Fuzzing Overhead through Coverage-Guided TracingPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2019
- HawkeyePublished by Association for Computing Machinery (ACM) ,2018
- Fuzzing: State of the ArtIEEE Transactions on Reliability, 2018
- Directed Greybox FuzzingPublished by Association for Computing Machinery (ACM) ,2017
- Model-based whitebox fuzzing for program binariesPublished by Association for Computing Machinery (ACM) ,2016
- Dynamically Instrumenting the QEMU Emulator for Linux Process Trace Generation with the GDB DebuggerACM Transactions on Embedded Computing Systems, 2014