Machine Learning for Security and Security for Machine Learning with Nicole Nichols - TWiML Talk #252
Today we’re joined by Nicole Nichols, a senior research scientist at the Pacific Northwest National Lab.
Nicole joined me to discuss her recent presentation at GTC, which was titled “Machine Learning for Security, and Security for Machine Learning.” Our conversation explores the two use cases she presented, insider threat detection, and software fuzz testing. We discuss the effectiveness of standard and bidirectional RNN language models for detecting malicious activity within the Los Alamos National Laboratory cybersecurity dataset, the augmentation of software fuzzing techniques using deep learning, and light-based adversarial attacks on image classification systems. I’d love to hear your thoughts on these use cases!
The complete show notes for this episode can be found at https://twimlai.com/talk/252.
Visit twimlai.com/gtc19 for more from our GTC 2019 series.
To learn more about Dell Precision workstations, and some of the ways they’re being used by customers in industries like Media and Entertainment, Engineering and Manufacturing, Healthcare and Life Sciences, Oil and Gas, and Financial services, visit Dellemc.com/Precision.
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