Dynamic fuzz testing of UAV configuration parameters based on dual guidance of fitness and coverage

References

• ArduPilot. (2023). ArduPilot code coverage report. Retrieved January 16, 2023, from https://firmware.ardupilot.org/coverage/
   Google Scholar
• Bai, S., Kolter, J. Z., & Koltun, V. (2018). An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv preprint arXiv:1803.01271.
   Google Scholar
• Bohme, M., Pham, V. T., Nguyen, M. D., & Roychoudhury, A. (2017). Directed greybox fuzzing. In: Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security (CCS ‘17). Association for Computing Machinery, New York, NY, USA, pp. 2329–2344.
   Google Scholar
• Chen, H., Xue, Y., Li, Y., Chen, B., Xie, X., Wu, X., & Liu, Y. (2018). Hawkeye: Towards a desired directed grey-box fuzzer. In: Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security (CCS ‘18). Association for Computing Machinery, New York, NY, USA, pp. 2095–2108.
   Google Scholar
• Chen, Y., Poskitt, C. M., Sun, J., Adepu, S., & Zhang, F. (2019). Learning-Guided network fuzzing for testing cyber-physical system defences. In 34th IEEE/ACM international conference on automated software engineering (ASE), San Diego, CA, USA, pp. 962-973.
   Google Scholar
• Fioraldi, A., Maier, D., Eißfeldt, H., & Heuse, M. (2020). AFL++: Combining incremental steps of fuzzing research. In 14th USENIX workshop on offensive technologies (WOOT’20), Boston marriott copley place in Boston, MA, USA.
   Google Scholar
• Geatpy. (2020). The Genetic and Evolutionary Algorithm Toolbox for Python with High Performance. Retrieved January 22, 2023, from http://geatpy.com/index.php/home/.
   Google Scholar
• Han, R., Yang, C., Ma, S., Ma, J., Sun, C., Li, J., & Elisa, B. (2022). Control parameters considered harmful: Detecting range specification bugs in drone configuration modules via learning-guided search. In 2022 IEEE/ACM 44th International Conference on Software Engineering (ICSE), Pittsburgh, PA, USA, pp. 462-473.
   Google Scholar
• Kim, H., Ozmen, M. O., Bianchi, A., Celik, Z. B., & Xu, D. (2021). Pgfuzz: Policy-guided fuzzing for robotic vehicles. In network and distributed system security symposium (NDSS), San Diego, CA, USA.
   Google Scholar
• Kim, T., Kim, C. H., Rhee, J., Fan, F., Zhan, T., Gregory, W., Zhang, X., Deng, X., & Xu, D. (2019). Rvfuzzer: Finding input validation bugs in robotic vehicles through control-guided testing. In 28th USENIX Security Symposium (USENIX Security 19) (pp. 425–442). USENIX Association.
   Google Scholar
• Lee, G., Shim, W., & Lee, B. (2021). Constraint-guided directed greybox fuzzing. In: 30th USENIX Security Symposium (USENIX Security 21), 3559–3576.
   Google Scholar
• Lemieux, C., & Sen, K. (2018). FairFuzz: a targeted mutation strategy for increasing greybox fuzz testing coverage. In Proceedings of the 33rd ACM/IEEE international conference on automated software engineering (ASE ‘18). Association for Computing Machinery, New York, NY, USA, pp. 475–485.
   Google Scholar
• OneFuzz. (2023). OneFuzz: A self-hosted Fuzzing-As-A-Service platform. Retrieved May 8, 2023, from https://github.com/microsoft/onefuzz
   Google Scholar
• Peng, H., Shoshitaishvili, Y., & Payer, M. (2018). T-Fuzz: fuzzing by program transformation. In 2018 IEEE Symposium on Security and Privacy (SP), San Francisco, CA, USA, pp. 697–710.
   Google Scholar
• Robert, S. (2023). Honggfuzz: Security oriented fuzzer with powerful analysis options. Retrieved May 8, 2023, form https://github.com/google/honggfuzz.
   Google Scholar
• Schumilo, S., Aschermann, C., Gawlik, R., Schinzel, S., & Holz, T. (2017). kAFL: Hardware-Assisted feedback fuzzing for OS kernels. In 26th USENIX security symposium (USENIX Security 17), Vancouver, BC, CAN, pp. 167–182.
   Google Scholar
• SparkFun Electronics. (2022). Retrieved August 22, 2022, from https://avc.sparkfun.com/2013
   Google Scholar
• Wang, Y. (2021). Research and implementation of drone parameter validation technique based on mixture density network and fuzzing. Xidian University.
   Google Scholar
• Yan, G., Lu, J., Shu, Z., & Kucuk, Y. (2017). Exploitmeter: Combining fuzzing with machine learning for automated evaluation of software exploitability. 2017 IEEE symposium on privacy-aware computing (PAC), Washington, DC, USA, pp. 164-175.
   Google Scholar
• Ye, X. (2020). Research on UAV system security vulnerability discovering based on fuzzing. Xidian University.
   Google Scholar