Securing AI and Advanced Topics
Build AI cybersecurity skills through fraud detection training and protection against emerging threats.
Build AI cybersecurity skills through fraud detection training and protection against emerging threats.
This course explores the cutting-edge intersection of artificial intelligence and cybersecurity, focusing on advanced techniques to secure AI systems against sophisticated threats. Participants will gain comprehensive knowledge of implementing AI-based solutions for credit card fraud detection in cloud environments while mastering the intricacies of Generative Adversarial Networks (GANs) for synthetic data generation. The curriculum provides hands-on experience with both black-box and white-box adversarial attacks, enabling learners to assess and enhance model resilience. Through practical implementations and real-world applications, students will develop expertise in feature engineering, model optimization, and performance evaluation specifically tailored for cybersecurity contexts. The course uniquely combines offensive and defensive strategies, preparing professionals to address complex challenges in the rapidly evolving landscape of AI security.
Instructors:
English
Not specified
Powered by
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
Develop practical skills in reinforcement learning for security applications
Implement and evaluate advanced algorithms for fraud detection
Skills you'll gain
This course includes:
1.3 Hours PreRecorded video
15 assignments
Access on Mobile, Tablet, Desktop
FullTime access
Shareable certificate
Get a Completion Certificate
Share your certificate with prospective employers and your professional network on LinkedIn.
Created by
Provided by
Top companies offer this course to their employees
Top companies provide this course to enhance their employees' skills, ensuring they excel in handling complex projects and drive organizational success.
There are 6 modules in this course
This comprehensive course explores the intersection of artificial intelligence and cybersecurity through six focused modules. Beginning with an introduction to the course framework, learners progress to practical applications of AI in fraud prevention using cloud-based solutions like IBM Watson. The curriculum then advances to Generative Adversarial Networks (GANs), teaching students how to implement these systems for creating synthetic data that closely resembles real datasets. A significant portion of the course addresses adversarial attacks, with hands-on implementations of both black-box and white-box techniques to understand vulnerabilities in AI systems. Later modules cover reinforcement learning applications in cybersecurity and data engineering techniques to optimize model performance. The course concludes with feature engineering methods and performance metrics specifically tailored to cybersecurity contexts, ensuring students can effectively evaluate and optimize AI models for security applications.
Course Introduction
Module 1 · 12 Minutes to complete
Fraud Prevention with Cloud AI Solutions
Module 2 · 2 Hours to complete
Introduction to Generative Adversarial Attacks (GANs)
Module 3 · 2 Hours to complete
GANs and Adversarial Attacks
Module 4 · 3 Hours to complete
Reinforcement Learning
Module 5 · 2 Hours to complete
Evaluating AI Models and Performance
Module 6 · 2 Hours to complete
Fee Structure
Instructor
Chair of Computer Science and Cybersecurity Programs
Lanier Watkins is the chair of the Johns Hopkins Engineering for Professionals Master's in Computer Science and Cybersecurity programs. He develops innovative algorithms and frameworks to address the evolving needs of defending critical infrastructure networks and systems. Watkins also holds a secondary appointment as an associate research professor with the JHU Information Security Institute, where he serves as the institute's assistant technical director. Prior to joining Johns Hopkins, he worked in industry for over ten years, initially at the Ford Motor Company and later at AT&T. His research focuses on key areas such as network security, Internet of Things (IoT) security, vulnerability monitoring, malware analysis, and data analytics. Watkins is a senior member of the Institute of Electrical and Electronics Engineers and has published numerous papers and holds several patents related to cybersecurity and AI. He received his PhD in Computer Science from Georgia State University and holds multiple master's degrees from Clark Atlanta University and Johns Hopkins University.
Testimonials
Testimonials and success stories are a testament to the quality of this program and its impact on your career and learning journey. Be the first to help others make an informed decision by sharing your review of the course.
Frequently asked questions
Below are some of the most commonly asked questions about this course. We aim to provide clear and concise answers to help you better understand the course content, structure, and any other relevant information. If you have any additional questions or if your question is not listed here, please don't hesitate to reach out to our support team for further assistance.