Course Overview
Your machine learning application works as intended, so you are done, right? But did you consider somebody poisoning your model by training it with intentionally malicious samples? Or sending specially-crafted input – indistinguishable from normal input – to your model that will get completely misclassified? Feeding in too large samples – for example, an image of 16Gbs to crash the application? Because that’s what the bad guys will do. And the list is far from complete.
As a machine learning practitioner, you need to be paranoid just as any developer out there. Interest in attacking machine learning solutions is gaining momentum, and therefore protecting against adversarial machine learning is essential. This needs not only awareness, but also specific skills to protect your ML applications. The course helps you gain these skills by introducing cutting edge attacks and protection techniques from the ML domain.
Machine learning is software after all. That’s why in this course we also teach common secure coding skills and discuss security pitfalls of the Python programming language. Both adversarial machine learning and core secure coding topics come with lots of hands on labs and stories from real life, all to provide a strong emotional engagement to security and to substantially improve code hygiene.
So that you are prepared for the forces of the dark side.
So that nothing unexpected happens.
Nothing.
Moyens d'évaluation :
- Quiz pré-formation de vérification des connaissances (si applicable)
- Évaluations formatives pendant la formation, à travers les travaux pratiques réalisés sur les labs à l’issue de chaque module, QCM, mises en situation…
- Complétion par chaque participant d’un questionnaire et/ou questionnaire de positionnement en amont et à l’issue de la formation pour validation de l’acquisition des compétences
Who should attend
Python developers working on machine learning systems
Prerequisites
General machine learning and Python development
Course Objectives
- Getting familiar with essential cyber security concepts
- Learning about various aspects of machine learning security
- Attacks and defense techniques in adversarial machine learning
- Identify vulnerabilities and their consequences
- Learn the security best practices in Python
- Input validation approaches and principles
- Managing vulnerabilities in third party components
- Understanding how cryptography can support appplication security
- Learning how to use cryptographic APIs correctly in Python
- Understanding security testing methodology and approaches
- Getting familiar with common security testing techniques and tools
Course Content
- Cyber security basics
- Machine learning security
- Input validation
- Security features
- Time and state
- Errors
- Using vulnerable components
- Cryptography for developers
- Security testing
- Wrap up
Anglais
Allemagne
Moyens Pédagogiques :