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.
Securing AI and Advanced Topics
Ce cours fait partie de Spécialisation AI for Cybersecurity
Instructeur : Lanier Watkins
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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 : Fraud Detection Techniques
- Catégorie : Adversarial Attack Implementation
- Catégorie : Model Evaluation and Optimization
- Catégorie : Generative Adversarial Networks (GANs)
- Catégorie : Reinforcement Learning Applications
Détails à connaître
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septembre 2024
15 devoirs
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Il y a 6 modules dans ce cours
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
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
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
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
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
Instructeur
Offert par
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Johns Hopkins University
Infosec
Board Infinity
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