Adversarial Machine Learning
Evan discusses the evolution of adversarial machine learning, highlighting its roots before GANs and the importance of model interpretability in cybersecurity. He emphasizes that understanding how models work can lead to better data collection and ultimately improve security measures. While neural networks may hold potential, the current focus remains on leveraging interpretable models and rich data sources to enhance insights into malicious activities.In this clip
From this podcast
The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
Machine Learning in Cybersecurity with Evan Wright - #16
Related Questions
Can you give examples of adversarial attacks on machine learning models as discussed in the episode #52 - Unadversarial Examples (Hadi Salman, MIT) and the clip Neural Network Features?
What are adversarial attacks on machine learning models as discussed in the episode #52 - Unadversarial Examples (Hadi Salman, MIT) and the clip Neural Network Object Design?
What are adversarial attacks on machine learning models, as discussed in the episode #52 - Unadversarial Examples (Hadi Salman, MIT) and the clip Neural Network Object Design?