Abstract
Fuzzing has been proven to be an effective way of detecting security vulnerabilities and has become the standard technique for detection of vulnerabilities due to its significant advantages in terms of automation. However, even the most advanced techniques cannot effectively trigger software vulnerabilities and often result in invalid random variation. In this paper, we propose a targeted fuzzing strategy based on combination of neural networks and particle swarm optimization algorithm, provide direction for the sample variation and direct the sample population to the target position, make it easier to trigger vulnerabilities by strengthening the test intensity of the marked target vulnerable position. We evaluate the system from different angles and the results show that it can 1) increase the testing intensity of the target location; 2) trigger vulnerabilities more quickly.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Petsios, T., Zhao, J., Keromytis, A.D., Jana, S.: Slowfuzz: automated domain-independent detection of algorithmic complexity vulnerabilities (2017)
Eberhart, Shi, Y.: Particle swarm optimization: developments, applications and resources. In: Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No. 01TH8546). IEEE ((2002))
Bohme, M., Pham, V.T., Roychoudhury, A.: Coverage-based greybox fuzzing as markov chain. IEEE Trans. Softw. Eng. 45(5), 489–506 (2017)
Wang, T., Wei, T., Gu, G., Zou, W.: TaintScope: a checksum-aware directed fuzzing tool for automatic software vulnerability detection. In: 31st IEEE Symposium on Security and Privacy, S&P 2010, Berleley/Oakland, California, USA, 16–19 May 2010. IEEE (2010)
Ganesh, V., Leek, T., Rinard, M.: Taint-based directed whitebox fuzzing (2009)
Zhang, B., Li, M., Wu, B., Tang, C.: Method of binary oriented fuzzy testing based on dynamic taint analysis. Mod. Electron. Tech. 37(19), 89–94 (2014)
Chaudhuri, S., Solar-Lezama, A.: Smoothing a program soundly and robustly. In: Proceedings of Computer Aided Verification - 23rd International Conference, CAV 2011, Snowbird, UT, USA, 14–20 July 2011. Springer, Heidelberg (2011)
She, D., Pei, K., Epstein, D., Yang, J., Ray, B., Jana, S.: NEUZZ: efficient fuzzing with neural program smoothing (2018)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of ICNN 1995 - International Conference on Neural Networks. IEEE (1995)
Funahashi, K.-I.: On the approximate realization of continuous mappings by neural networks. Neural Netw. 2(3), 183–192 (1989)
Ratanavilisagul, C., Kruatrachue, B.: A modified particle swarm optimization with mutation and reposition. Int. J. Innovative Comput. Inf. Control: IJICIC 10(6), 2127–2142 (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Wang, Y., Chen, C., Cui, B. (2021). A Targeted Fuzzing Technique Based on Neural Networks and Particle Swarm Optimization. In: Barolli, L., Poniszewska-Maranda, A., Park, H. (eds) Innovative Mobile and Internet Services in Ubiquitous Computing . IMIS 2020. Advances in Intelligent Systems and Computing, vol 1195. Springer, Cham. https://doi.org/10.1007/978-3-030-50399-4_36
Download citation
DOI: https://doi.org/10.1007/978-3-030-50399-4_36
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-50398-7
Online ISBN: 978-3-030-50399-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)