Automated software vulnerability testing using in-depth training methods
Abstract
Theoretical information about testing of software using fuzzing method. The technologies of reinforcement training and intellectual fuzzing in the software testing process. An algorithm is described with the help of which the indicated methods and technologies are realized. Statistical results of studies that were conducted during the testing of some programs and utilities intended for everyday use, as well as the program developed by the students are offered.
Downloads
References
Sutton M., Greene A., Amini P. Fuzzing: Brute Force Vulnerability Discovery. Boston, MA, USA: Addison-Wesley Professional, 2007. URL: https://www.oreilly.com/library/view/fuzzing-brute-force/9780321446114/
AlphaGo Games – English. URL: https://deepmind.com/research/alphago/match-archive/alphago-games-english/
Sutton R.S., Barto A.G. Reinforcement learning: An introduction. MIT press Cambridge, 1998. URL: http://incompleteideas.net/book/bookdraft2017nov5.pdf
Kingma D. P., Ba J. Adam: A Method for Stochastic Optimization. URL: https://arxiv.org/pdf/1412.6980.pdf
t-kryterii Stiudenta. URL: http://fpo.bsmu.edu.ua/static/t-kryteriy-styudenta
Li Yu. Deep Reinforcement Learning: An Overview. URL:https://arxiv.org/pdf/1810.06339.pdf
Böttinger K., Godefroid P., Singh R. Deep Reinforcement Fuzzing. URL: https://arxiv.org/pdf/1801.04589.pdf
Deviniak О. Statystychni hipotezy ta yikh perevirka. 2014. URL: http://stat.org.ua/statclasses/hypotheses-testing/Deviniak Statystychni hipotezy ta yikh perevirka
