Securing Your AI and Machine Learning Systems
Course
Online
Description
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Type
Course
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Methodology
Online
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Start date
Different dates available
Design secure AI/ML solutionsArtificial 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:About the Author
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Alexander Polyakov is a cybersecurity expert and serial entrepreneur. He has over 15 years' practical experience in AI cybersecurity and other different fields, such as pentesting, security engineering, product management, architectures, and technology leadership. He is a member of Forbes Technology Council and a Forbes columnist, where he publishes his vision for the future. He has been recognized as Entrepreneur and R&D Professional of the Year by various bodies. His expertise covers cybersecurity aspects of various complex systems from enterprise applications and industry-specific systems to AI, ML, and future technologies
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Start date
About this course
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
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Subjects
- Options
- Artificial Intelligence
- Works
- Approach
- Design
- Perspective
- Systems
- Technology
- IT
- IT Management
Course programme
- Give a clear picture of artificial intelligence and machine learning
- Provide machine learning terminologies and general classification
- Present an overview of the machine learning attacks
- Provide potential options to set up the environment
- Describe the options in detail
- Summarize all requirements
- Give a clear picture of artificial intelligence and machine learning
- Provide machine learning terminologies and general classification
- Present an overview of the machine learning attacks
- Provide potential options to set up the environment
- Describe the options in detail
- Summarize all requirements
- Give a clear picture of artificial intelligence and machine learning
- Provide machine learning terminologies and general classification
- Present an overview of the machine learning attacks
- Give a clear picture of artificial intelligence and machine learning
- Provide machine learning terminologies and general classification
- Present an overview of the machine learning attacks
- Give a clear picture of artificial intelligence and machine learning
- Provide machine learning terminologies and general classification
- Present an overview of the machine learning attacks
- Give a clear picture of artificial intelligence and machine learning
- Provide machine learning terminologies and general classification
- Present an overview of the machine learning attacks
- Give a clear picture of artificial intelligence and machine learning
- Provide machine learning terminologies and general classification
- Present an overview of the machine learning attacks
- Give a clear picture of artificial intelligence and machine learning
- Provide machine learning terminologies and general classification
- Present an overview of the machine learning attacks
- Provide potential options to set up the environment
- Describe the options in detail
- Summarize all requirements
- Provide potential options to set up the environment
- Describe the options in detail
- Summarize all requirements
- Provide potential options to set up the environment
- Describe the options in detail
- Summarize all requirements
- Provide potential options to set up the environment
- Describe the options in detail
- Summarize all requirements
- Provide potential options to set up the environment
- Describe the options in detail
- Summarize all requirements
- Provide potential options to set up the environment
- Describe the options in detail
- Summarize all requirements
- Introduce the rest of the machine learning tasks
- Describe each machine learning task in detail
- Summarize and provide the overall picture
- Describe why adversarial attacks exist in ML models
- Provide a theoretical proof
- Present an overview of machine learning attacks
- List the machine learning tasks from an attacker’s perspective
- Describe how each machine learning task can be hacked
- Summarize all the examples and prove the initial idea
- Explain why adversarial attacks take place
- Describe what should be done to perform an attack
- Show a step-by-step approach for attacking a machine learning model
- Download and configure the scripts
- Run attack scripts against a vulnerable machine learning model
- Experiment with different configuration values
- Introduce the rest of the machine learning tasks
- Describe each machine learning task in detail
- Summarize and provide the overall picture
- Describe why adversarial attacks exist in ML models
- Provide a theoretical proof
- Present an overview of machine learning attacks
- List the machine learning tasks from an attacker’s perspective
- Describe how each machine learning task can be hacked
- Summarize all the examples and prove the initial idea
- Explain why adversarial attacks take place
- Describe what should be done to perform an attack
- Show a step-by-step approach for attacking a machine learning model
- Download and configure the scripts
- Run attack scripts against a vulnerable machine learning model
- Experiment with different configuration values
- Introduce the rest of the machine learning tasks
- Describe each machine learning task in detail
- Summarize and provide the overall picture
- Introduce the rest of the machine learning tasks
- Describe each machine learning task in detail
- Summarize and provide the overall picture
- Introduce the rest of the machine learning tasks
- Describe each machine learning task in detail
- Summarize and provide the overall picture
- Introduce the rest of the machine learning tasks
- Describe each machine learning task in detail
- Summarize and provide the overall picture
- Introduce the rest of the machine learning tasks
- Describe each machine learning task in detail
- Summarize and provide the overall picture
- Introduce the rest of the machine learning tasks
- Describe each machine learning task in detail
- Summarize and provide the overall picture
- Describe why adversarial attacks exist in ML models
- Provide a theoretical proof
- Present an overview of machine learning attacks
- Describe why adversarial attacks exist in ML models
- Provide a theoretical proof
- Present an overview of machine learning attacks
- Describe why adversarial attacks exist in ML models
- Provide a theoretical proof
- Present an overview of machine learning attacks
- Describe why adversarial attacks exist in ML models
- Provide a theoretical proof
- Present an overview of machine learning attacks
- Describe why adversarial attacks exist in ML models
- Provide a theoretical proof
- Present an overview of machine learning attacks
- Describe why adversarial attacks exist in ML models
- Provide a theoretical proof
- Present an overview of machine learning attacks
- List the machine learning tasks from an attacker’s perspective
- Describe how each machine learning task can be hacked
- Summarize all the examples and prove the initial idea
- List the machine learning tasks from an attacker’s perspective
- Describe how each machine learning task can be hacked
- Summarize all the examples and prove the initial idea
- List the machine learning tasks from an attacker’s perspective
- Describe how each machine learning task can be hacked
- Summarize all the examples and prove the initial idea
Additional information
Securing Your AI and Machine Learning Systems