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Securing AI and Advanced Topics

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Securing AI and Advanced Topics

Name: Securing AI and Advanced Topics
Rating: 4.36 (25 reviews)

Ce cours fait partie de Spécialisation "AI for Cybersecurity"

Instructeur : Lanier Watkins

2 586 déjà inscrits

Inclus avec

6 modules

Obtenez un aperçu d'un sujet et apprenez les principes fondamentaux.

25 avis

niveau Intermédiaire

Expérience recommandée

2 semaines à compléter

à 10 heures par semaine

Planning flexible

Apprenez à votre propre rythme

6 modules

Obtenez un aperçu d'un sujet et apprenez les principes fondamentaux.

25 avis

niveau Intermédiaire

Expérience recommandée

2 semaines à compléter

à 10 heures par semaine

Planning flexible

Apprenez à votre propre rythme

Ce que vous apprendrez

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.

Compétences que vous acquerrez

Catégorie : Model Optimization
Catégorie : Model Evaluation
Catégorie : Reinforcement Learning
Catégorie : Feature Engineering
Catégorie : Data Synthesis
Catégorie : AI Security
Catégorie : Cyber Security Strategy
Catégorie : Cybersecurity

Outils que vous découvrirez

Catégorie : Fraud detection
Catégorie : Generative Adversarial Networks (GANs)

Détails à connaître

Certificat partageable

Ajouter à votre profil LinkedIn

Évaluations

15 devoirs

Enseigné en Anglais

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logos de Petrobras, TATA, Danone, Capgemini, P&G et L'Oreal

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Ce cours fait partie de la Spécialisation "AI for Cybersecurity"

Lorsque vous vous inscrivez à ce cours, vous êtes également inscrit(e) à cette Spécialisation.

Apprenez de nouveaux concepts auprès d'experts du secteur
Acquérez une compréhension de base d'un sujet ou d'un outil
Développez des compétences professionnelles avec des projets pratiques
Obtenez un certificat professionnel partageable

Il y a 6 modules dans ce cours

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.

Détails du module

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.

Inclus

2 lectures

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.

Inclus

2 vidéos3 lectures3 devoirs

2 vidéosTotal 14 minutes

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

3 lecturesTotal 65 minutes

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

3 devoirsTotal 90 minutes

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

Inclus

2 vidéos3 lectures3 devoirs

2 vidéosTotal 17 minutes

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

3 lecturesTotal 70 minutes

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

3 devoirsTotal 90 minutes

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

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

Inclus

2 vidéos3 lectures3 devoirs1 laboratoire non noté

2 vidéosTotal 18 minutes

Adversarial Attacks Explained8 minutes
Hands-On Adversarial Attacks9 minutes

3 lecturesTotal 65 minutes

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

3 devoirsTotal 90 minutes

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

1 laboratoire non notéTotal 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.

Inclus

2 vidéos3 lectures3 devoirs

2 vidéosTotal 14 minutes

Reinforcement Learning8 minutes
Evading Malware Detectors with RL6 minutes

3 lecturesTotal 65 minutes

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

3 devoirsTotal 90 minutes

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

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

Inclus

2 vidéos3 lectures3 devoirs

2 vidéosTotal 17 minutes

Challenges with Using AI for Cybersecurity9 minutes
Evaluating AI Models8 minutes

3 lecturesTotal 65 minutes

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

3 devoirsTotal 90 minutes

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

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