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Securing AI and Advanced Topics

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Securing AI and Advanced Topics

This course is part of AI for Cybersecurity Specialization

Instructor: Lanier Watkins

2,579 already enrolled

Included with

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6 modules

Gain insight into a topic and learn the fundamentals.

25 reviews

Intermediate level

Recommended experience

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

6 modules

Gain insight into a topic and learn the fundamentals.

25 reviews

Intermediate level

Recommended experience

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

What you'll learn

Learn to implement AI-based solutions to detect and prevent credit card fraud in cloud environments.
Explore the fundamentals of Generative Adversarial Networks and their applications in generating synthetic data.
Gain hands-on experience with black-box and white-box adversarial attacks to assess and enhance model resilience.
Master techniques in feature engineering and performance evaluation to optimize AI models for cybersecurity applications.

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

15 assignments

Taught in English

91% of learners achieved a positive career outcome

See how employees at top companies are mastering in-demand skills

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Build your subject-matter expertise

This course is part of the AI for Cybersecurity Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 6 modules in this course

In the course "Securing AI and Advanced Topics", learners will delve into the cutting-edge intersection of AI and cybersecurity, focusing on how advanced techniques can secure AI systems against emerging threats. Through a structured approach, you will explore practical applications, including fraud prevention using cloud AI solutions and the intricacies of Generative Adversarial Networks (GANs). Each module builds upon the previous one, enabling a comprehensive understanding of both offensive and defensive strategies in cybersecurity.

What sets this course apart is its hands-on experience with real-world implementations, allowing you to design effective solutions for detecting and mitigating fraud, as well as understanding adversarial attacks. By evaluating AI models and learning reinforcement learning principles, you will gain insights into enhancing cybersecurity measures. Completing this course will equip you with the skills necessary to address complex challenges in the evolving landscape of AI and cybersecurity, making you a valuable asset in any organization. Whether you are seeking to deepen your expertise or enter this critical field, this course provides the tools and knowledge you need to excel.

Module details

This course provides a comprehensive exploration of AI-based solutions for credit card fraud detection, emphasizing the implementation and evaluation of advanced algorithms, including Generative Adversarial Networks (GANs). Students will gain practical experience in executing adversarial attacks and optimizing machine learning models, enhancing their ability to develop robust AI systems. Through hands-on projects, participants will synthesize knowledge to address real-world challenges in fraud detection and model resilience.

What's included

2 readings

In this module, we study the background of threats that prevent credit card fraud. Then, we investigate hands-on credit card fraud detection implementations. Also, we discuss metrics to evaluate the performance of credit card fraud detection algorithms.

What's included

2 videos3 readings3 assignments

2 videosTotal 14 minutes

Credit Card Fraud Prevention with AI4 minutes
Credit Card Fraud Prevention: IBM Watson Example10 minutes

3 readingsTotal 65 minutes

Reading References10 minutes
Reading References10 minutes
Self-Reflective Reading: Understanding of AI Fraud Prevention Tools45 minutes

3 assignmentsTotal 90 minutes

Credit Card Fraud Threats and AI Prevention15 minutes
Implementing and Evaluating IBM Watson for Fraud Detection15 minutes
Fraud Prevention with Cloud AI Solutions60 minutes

In this module, we study generative adversarial networks (GANs) background. Then, we investigate a hands-on GAN implementation and how it can be used to develop synthetic data likely indistinguishable from the real data.

What's included

2 videos3 readings3 assignments

2 videosTotal 17 minutes

Introduction to Generative Adversarial Networks (GANs)9 minutes
Getting to Know GANs8 minutes

3 readingsTotal 70 minutes

Reading References15 minutes
Reading References10 minutes
Self-Reflective Reading: Research GANs45 minutes

3 assignmentsTotal 90 minutes

Fundamentals of Generative Adversarial Networks (GANs)15 minutes
Hands-On GAN Implementation and Synthetic Data Generation15 minutes
Introduction to Generative Adversarial Attacks (GANs)60 minutes

In this module, we will discuss black and white-box adversarial attacks. Also, we will explore hands-on implementations of several adversarial attacks.

What's included

2 videos3 readings3 assignments1 ungraded lab

2 videosTotal 18 minutes

Adversarial Attacks Explained8 minutes
Hands-On Adversarial Attacks9 minutes

3 readingsTotal 65 minutes

Reading References10 minutes
Reading References10 minutes
Self-Reflective Reading: GANs and Adversarial Attacks45 minutes

3 assignmentsTotal 90 minutes

Understanding Black-box and White-box Adversarial Attacks15 minutes
Practical Implementation of Adversarial Attacks15 minutes
GANs and Adversarial Attacks60 minutes

1 ungraded labTotal 60 minutes

Practice Lab: Generating Synthetic QR Codes with the Trained Generator60 minutes

In this module we will study reinforcement learning (RL) and how it can be used for adversarial attacks. Also, we will study data engineering techniques to optimize datasets to help improve ML model performance.

What's included

2 videos3 readings3 assignments

2 videosTotal 14 minutes

Reinforcement Learning8 minutes
Evading Malware Detectors with RL6 minutes

3 readingsTotal 65 minutes

Reading References10 minutes
Reading References10 minutes
Self-Reflective Reading: Understanding Reinforcement Learning45 minutes

3 assignmentsTotal 90 minutes

Reinforcement Learning and its Applications15 minutes
Using RL for Adversarial Attacks and Optimizing Datasets15 minutes
Reinforcement Learning60 minutes

In this module, we will discuss feature engineering and model optimization techniques. Also, we will explore ML model performance metrics.

What's included

2 videos3 readings3 assignments

2 videosTotal 17 minutes

Challenges with Using AI for Cybersecurity9 minutes
Evaluating AI Models8 minutes

3 readingsTotal 65 minutes

Reading References10 minutes
Reading References10 minutes
Self-Reflective Reading: Feature Engineering in Cybersecurity Applications45 minutes

3 assignmentsTotal 90 minutes

Feature Engineering and Model Optimization Techniques15 minutes
Evaluating AI Models and Performance Metrics15 minutes
Evaluating AI Models and Performance60 minutes

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Instructor

Instructor ratings

(6 ratings)

Lanier Watkins

Johns Hopkins University

3 Courses11,327 learners

Offered by

Johns Hopkins University

Securing AI and Advanced Topics

Securing AI and Advanced Topics

What you'll learn

Skills you'll gain

Tools you'll learn

Details to know

See how employees at top companies are mastering in-demand skills

Build your subject-matter expertise

There are 6 modules in this course

Course Introduction

What's included

Fraud Prevention with Cloud AI Solutions

What's included

Introduction to Generative Adversarial Attacks (GANs)

What's included

GANs and Adversarial Attacks

What's included

Reinforcement Learning

What's included

Evaluating AI Models and Performance

What's included

Earn a career certificate

Instructor

Offered by

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Frequently asked questions

When will I have access to the lectures and assignments?

What will I get if I subscribe to this Specialization?

Is financial aid available?

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