Finding the Badness: Deep Learning in Cybersecurity
In the area of Cybersecurity, data scientists often strive to "find the badness", or identify potential threats before they become problematic.
In this work, we’ll discuss the application of a character-level recurrent neural network (https://en.wikipedia.org/wiki/Recurrent_neural_network) (RNN) to the task of "finding the badness" in web proxy logs through early identification of potentially malicious domains.
Zachary Brown received a PhD in computational physics from The College of William & Mary in 2014, and has been working in the data and analytics space since. His interests include deep learning and NLP and their application to the fields of cybersecurity, politics, and healthcare. He is currently working as a Lead Data Scientist on the Enterprise Cyberdefense team at Unitedhealth Group.
About Data Hackers
RVA Data Hackers is a community of programmers who meet regularly to develop skills and learn about the tools and techniques of Big Data. We discuss how to find, organize, understand and serve data sets large and small. We'll cover anything of interest to our members -- machine learning, artificial intelligence, civic data, data viz, and architectures to scale data processing. Topics vary from basic concepts to demonstrations of real-world implementations and everything in between. Our mission is to foster a local community of experienced, practicing experts. We're here to have fun, share, and learn about an exciting field of computer science. Come on down and help us build Richmond's Data Hacking community.
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