University of California

Setareh Rafatirad

Prasant Mohapatra

Han Wang

Houman Homayoun

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.

Growing community of inventors

San Jose, CA, United States of America

Setareh Rafatirad