University of California

Houman Homayoun

Prasant Mohapatra

Setareh Rafatirad

Han Wang

The embodiments disclose a system and method including a random number generator coupled to the randomized machine learning-based malware detector configured for determining changes of settings and selections of parameters, candidate classifiers integrated with the randomized machine learning-based malware detector and configured to be initiated by a random number to avoid transferable learning, a set of feature combinations for random feature selection including monitoring granularity and detection prediction latency and a random number for identifying a set of feature combinations that minimize the overhead and maintain enough variance in data for baffling the malware adversarial attacks.

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San Jose, CA, United States of America

Houman Homayoun