Class Central is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

YouTube

Building Realistic Machine Learning Systems for Security

USENIX Enigma Conference via YouTube

Overview

Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only!
Explore the challenges and strategies for building robust machine learning systems for security in this 19-minute conference talk from USENIX Enigma 2020. Delve into the complexities of developing effective malware detectors using machine learning, addressing issues such as achieving low false positive rates, dealing with polluted ground truth data, and testing dynamic models against ephemeral malware. Learn about modeling realistic adversaries for adversarial attacks and defenses, and gain insights into the practical considerations for implementing machine learning in cybersecurity contexts.

Syllabus

Intro
Machine Learning is necessary for detecting malware at scale
Let's build a malware detector using machine learning
What is malware?
Professional Heuristics for Ground Truth
Does the overall performance of the classifiers matter?
Adversarial attacks: feature space vs problem space
Are adversarial attacks harmful to users?
Is evading one classifier enough?
Who is the adversary?
Questions?

Taught by

USENIX Enigma Conference

Reviews

Start your review of Building Realistic Machine Learning Systems for Security

Never Stop Learning.

Get personalized course recommendations, track subjects and courses with reminders, and more.

Someone learning on their laptop while sitting on the floor.