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Threats from the internet, particularly malicious software (i.e., malware)often use cryptographic algorithms to disguise their actions and even to takecontrol of a victim's system (as in the case of ransomware). Malware and otherthreats proliferate too quickly for the time-consuming traditional methods ofbinary analysis to be effective. By automating detection and classification ofcryptographic algorithms, we can speed program analysis and more efficientlycombat malware.  This thesis will present several methods of leveraging machine learning toautomatically discover and classify cryptographic algorithms in compiled binaryprograms.  While further work is necessary to fully evaluate these methods on real-worldbinary programs, the results in this paper suggest that machine learning can beused successfully to detect and identify cryptographic primitives in compiledcode. Currently, these techniques successfully detect and classifycryptographic algorithms in small single-purpose programs, and further work isproposed to apply them to real-world examples.

Automated detection and classification of cryptographic algorithms in binary programs through machine learning