Submitting this abstract for an invited talk.
The Laboratory for Physical Sciences has recently been conducting research in ML model uncertainty and confidence, detecting out-of-distribution data, and detecting concept drift. As we deploy ML models into operations, we must be constantly assessing whether the models are still effective and performing as expected in the current data environment. This is relevant in all cases, but especially critical in cybersecurity applications, because the data, technology, actors and behaviors are all evolving so rapidly. This talk will review several algorithmic techniques developed to address this problem.
The Laboratory for Physical Sciences is a DOD lab performing research in quantum computing, novel computer architectures, high performance computing, brain-inspired systems for learning, and application of machine learning to cybersecurity problems. This talk will provide an overview of ongoing research efforts at the lab, and then will drill down into work applying machine learning and other techniques to the task of malware analysis. This includes development of classifiers to determine if a given file is malware, generation of features through static analysis, disassembly, decompilation and dynamic analysis, aides to the human malware reverse engineer, and automated signature generation for family identification.
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