The article provides a view on modern technologies, which are used for automatic software vulnerability testing in critically important systems. Features of fuzzing realization (which is based on making many inputs with different mutated data) are also studied. As a result, testing algorithm picks input data that is more likely to cause a fail or incorrect work of software product. Deep learning algorithms are used to decrease the computational complexity of testing process. The use of simple fuzzer and Deep Reinforcement Learning algorithm shows that the amount of mutations necessary to find vulnerabilities decreases by 30%.
Automated software vulnerability testing using in-depth training methods
Kuznetsov
;
2019-01-01
Abstract
The article provides a view on modern technologies, which are used for automatic software vulnerability testing in critically important systems. Features of fuzzing realization (which is based on making many inputs with different mutated data) are also studied. As a result, testing algorithm picks input data that is more likely to cause a fail or incorrect work of software product. Deep learning algorithms are used to decrease the computational complexity of testing process. The use of simple fuzzer and Deep Reinforcement Learning algorithm shows that the amount of mutations necessary to find vulnerabilities decreases by 30%.File in questo prodotto:
Non ci sono file associati a questo prodotto.
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
