Applying Machine Learning to Network Anomalies | Part 3

Applying Machine Learning to Network Anomalies | Part 3

Channel:
United States SANS Cyber Defense
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20,000
Published on ● Video Link: https://www.youtube.com/watch?v=PdddO1-jeQQ


Duration: 59:18
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In this episode, we are finally ready to build a neural network! David will walk through creating an autoencoder network using Tensorflow to distinguish one type of network traffic from another. Along the way, we’ll look at how to use the data from the Zeek Broker interface in real-time to classify arbitrary network data from Python.

David Hoelzer, the operations chief for Enclave Forensics, Inc. and a managing partner with Occulumen, Ltd. (and SANS Fellow) will lead this livestream. David has more than thirty years of experience in the IT and cybersecurity fields, with more than 25 years specifically in the network monitoring, SOC operations, and incident response fields. He leads the machine learning function within Enclave Forensics and is the author of both SEC503 (Intrusion Detection In-Depth) and SEC595 (Applied Data Science and Machine Learning/AI for Cybersecurity Professionals).

https://www.sans.org/cyber-security-courses/intrusion-detection-in-depth/
https://www.sans.org/cyber-security-courses/applied-data-science-machine-learning/

#tensorflow #machinelearning #ai #ml #zeek #networking #datascience #python



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