Methods, systems, and computer readable media for training a machine learning (ml) model using fuzz test data

A system for training a machine learning model using fuzz test data, the system comprising:

• a memory;
• at least one processor;
• and
• a test system implemented using the memory and the at least one processor, the test system configured for:
• performing, using test configuration information, a plurality of fuzz testing sessions involving one or more systems under test (SUT), wherein at least some of the plurality of fuzz testing sessions include different test traffic parameters and/or SUT configurations than at least one of the plurality of fuzz testing sessions;
• obtaining fuzz test data from one or more sources, wherein the fuzz test data includes test traffic data and SUT performance data associated with the plurality of fuzz testing sessions;
• training, using the fuzz test data and one or more machine learning algorithms, a machine learning model for receiving as input traffic data associated with test traffic or live traffic involving a respective SUT and SUT performance data associated with the test traffic or live traffic and providing as output a stress state value indicating the likelihood of the respective SUT crashing or failing;
• and
• storing, in a machine learning model data store, the trained machine learning model for subsequent use by the test system or a SUT analyzer.

A non-transitory computer readable medium comprising computer executable instructions embodied in the non-transitory computer readable medium that when executed by a processor of a computer perform steps comprising:

• at a test system:
• performing, using test configuration information, a plurality of fuzz testing sessions involving one or more systems under test (SUT), wherein at least some of the plurality of fuzz testing sessions include different test traffic parameters and/or SUT configurations than at least one of the plurality of fuzz testing sessions;
• obtaining fuzz test data from one or more sources, wherein the fuzz test data includes test traffic data and SUT performance data associated with the plurality of fuzz testing sessions;
• training, using the fuzz test data and one or more machine learning algorithms, a machine learning model for receiving as input traffic data associated with test traffic or live traffic involving a respective SUT and SUT performance data associated with the test traffic or live traffic and providing as output a stress state value indicating the likelihood of the respective SUT crashing or failing;
• and
• storing, in a machine learning model data store, the trained machine learning model for subsequent use by the test system, a network analyzer, or a device.