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The Secure Sockets Layer (SSL) and Transport Layer Security (TLS) protocolsare the foundation of network security. The certificate verification in SSL/TLSimplementations is vital and may become the weak link in the whole networkecosystem. In previous works, some research focused on the automated testing ofcertificate verification, and the main approaches rely on generating massivecertificates through randomly combining parts of seed certificates for fuzzing.Although the generated certificates could meet the semantic constraints, thecost is quite heavy, and the performance is limited due to the randomness. Tofill this gap, in this paper, we propose DRLGENCERT, the first framework ofapplying deep reinforcement learning to the automated testing of certificateverification in SSL/TLS implementations. DRLGENCERT accepts ordinarycertificates as input and outputs newly generated certificates which couldtrigger discrepancies with high efficiency. Benefited by the deep reinforcementlearning, when generating certificates, our framework could choose the bestnext action according to the result of a previous modification, instead ofsimple random combinations. At the same time, we developed a set of newtechniques to support the overall design, like new feature extraction methodfor X.509 certificates, fine-grained differential testing, and so forth. Also,we implemented a prototype of DRLGENCERT and carried out a series of real-worldexperiments. The results show DRLGENCERT is quite efficient, and we obtained84,661 discrepancy-triggering certificates from 181,900 certificate seeds, sayaround 46.5% effectiveness. Also, we evaluated six popular SSL/TLSimplementations, including GnuTLS, MatrixSSL, MbedTLS, NSS, OpenSSL, andwolfSSL. DRLGENCERT successfully discovered 23 serious certificate verificationflaws, and most of them were previously unknown.

DRLGENCERT: Deep Learning-based Automated Testing of Certificate Verification in SSL/TLS Implementations