Gain an intuitive understanding of deep neural networks in this 44-minute Black Hat conference talk. Explore the fundamental concepts that enable neural networks to learn from data and make accurate decisions about file safety, malicious URLs, and domain names. Delve into topics such as convolutional neural networks, feature spaces, and nonlinear decision boundaries. Learn about practical applications, including content generation, machine learning detection, and URL detection. Discover the role of GPUs in data science and gain insights into the field without becoming a data scientist. Conclude with guidance on further learning resources and next steps in the world of deep neural networks for cybersecurity applications.
Deep Neural Networks for Hackers - Methods, Applications, and Open Source Tools
Overview
Syllabus
Introduction
Team
What is Deep Learning
Generated Content
Neural Networks
Summary
Recipe Generator
Machine Learning Detection
Nonlinear Decision Boundary
Feature Spaces
Neural Networks Decision Boundaries
Recap
Kittens
Convolutional Neural Networks
Convolutional Neuron
Convolutional Layers
URL Detector
Weights
Character Patterns
Meme Generator
Cat Years
GPU
Data Science
Not Data Scientists
Where to Go
Wrap Up
Taught by
Black Hat
