English | Size: 637.62 MB
Genre: eLearning
Learn
Design secure AI solution architectures to cover all aspects of AI security from model to environment
Create a high-level threat model for AI solutions and choose the right priorities against various threats
Design specific security tests for image recognition systems
Test any AI system against the latest attacks with the help of simple tools
Learn the most important metrics to compare various attacks and defences
Deploy the right defence methods to protect AI systems against attacks by comparing their efficiency
Secure your AI systems with the help of practical open-source tools
About
Artificial Intelligence (AI) is literally eating software as more and more solutions become ML-based. Unfortunately, these systems also have vulnerabilities; but, compared to software security, few people are really knowledgeable about this area. If it’s impossible to secure AI against cyberattacks, there will be no AI-based technologies, such as self-driving cars, and yet another “AI winter” will soon be on us.
This course is almost certainly the first public, online, hands-on introduction to the future perspectives of cybersecurity and adopts a clear and easy-to-follow approach. In this course, you will learn about high-level risks targeting AI/ML systems. You will design specific security tests for image recognition systems and master techniques to test against attacks. You will then learn about various categories of adversarial attacks and how to choose the right defense strategy.
By the end of this course, you will be acquainted with various attacks and, more importantly, with the steps that you can take to secure your AI and machine learning systems effectively. For this course, practical experience with Python, machine learning, and deep learning frameworks is assumed, along with some basic math skills.
All the code and supporting files for this course are available on GitHub at:
https://github.com/PacktPublishing/Securing-Your-AI-and-Machine-Learning-Systems
Features
Gain practical experience with various open-source tools such as ART (Adversarial Robustness Toolkit) and DeepSec, developed to test machine learning algorithms for security
Learn to design secure AI solutions depending on risks that are typical for your application with the help of a unique approach
Understand the attacks and different approaches for securing various AI/ML systems
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