Adversarial Machine LearningLearn how to perform white-box and black-box attacks on machine learning classifiers in this course.
Course descriptionThis course begins by providing an overview of white-box and black-box adversarial attacks on machine learning systems. It will then guide you through using the Fast Gradient Signed Method (FGSM) white-box attack on a keras machine learning model. Next, we will cover black-box attacks. You will be guided on using a machine learning as a service system called Clarif.AI and then performing a black-box adversarial attack to trick this service into labeling a benign image as dangerous. Finally, to solidify learning, the student is given an assignment on tricking a MNIST keras classifier via a white-box adversarial attack.
File - 01:00:00
Assignment: TrickMe video
Video - 00:01:00
Black-Box Attack on Clarif.AI
Video - 00:21:00
Getting Started with Clarif.AI
Video - 00:03:00
White-Box Attacks on Machine Learning
Video - 00:06:00
Adversarial Machine Learning
Video - 00:02:00
Associated NICE Work Roles
All Infosec training maps directly to the NICE Workforce Framework for Cybersecurity to guide you from beginner to expert across 52 Work Roles.
- Law Enforcement / Counterintelligence Forensics Analyst
- Cyber Defense Forensics Analyst
- Data Analyst
Plans & pricing
- Team administration and reporting
- Dedicated client success manager
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Easily authenticate and manage your learners by connecting to any identity provider that supports the SAML 2.0 standard.
Integrations via API
Retrieve training performance and engagement metrics and integrate learner data into your existing LMS or HRS.
- 190+ role-guided learning paths and assessments (e.g., Incident Response)
- 100s of hands-on labs in cloud-hosted cyber ranges
- Create and assign custom learning paths
- Custom certification practice exams (e.g., CISSP, CISA)
- Optional upgrade: Guarantee team certification with live boot camps