Abstract: 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.

Abstract: The embodiments disclose a system and method including a side-channel attack detection framework comprising a data detector and a distribution detector configured for detecting known and unknown side-channel attack on a user's computer, a data detector configured for constantly monitoring the user's computer microarchitectural features activities in real-time, wherein the data detector includes a machine learning-based classification system and a distribution detector data distribution model configured for detecting both known and unknown emerging side-channel attacks in real-time.

Abstract: 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.