Johns Hopkins University
AI for Cybersecurity Specialization
Johns Hopkins University

AI for Cybersecurity Specialization

Master AI Techniques for Cybersecurity Challenges. Develop expertise in advanced AI techniques to detect and prevent cybersecurity threats, ensuring robust protection against evolving digital risks.

Lanier Watkins

Instructor: Lanier Watkins

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Get in-depth knowledge of a subject
Intermediate level

Recommended experience

3 months
at 5 hours a week
Flexible schedule
Learn at your own pace
Get in-depth knowledge of a subject
Intermediate level

Recommended experience

3 months
at 5 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • Implement AI-driven techniques for detecting and mitigating advanced malware and network anomalies effectively.

  • Utilize Generative Adversarial Networks (GANs) to understand and counteract adversarial attacks in AI systems.

  • Evaluate AI model performance and apply reinforcement learning to enhance adaptive cybersecurity measures.

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Taught in English
Recently updated!

September 2024

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Specialization - 3 course series

What you'll learn

  • Use AI techniques to detect and mitigate various cyber threats, protecting digital assets and data.

  • Develop and apply machine learning models to identify, classify, and filter spam and phishing emails.

  • Implement AI-driven biometric solutions like keystroke dynamics and facial recognition to enhance user authentication security.

Skills you'll gain

Category: AI Applications in Cybersecurity
Category: Cyber Threat Risk Management
Category: AI-Driven User Authentication
Category: Hands-on ML Model Development
Category: Spam and Phishing Detection

What you'll learn

  • Understand various types of malware and apply foundational analysis techniques to effectively detect and classify them.

  • Implement advanced machine learning algorithms, including clustering and decision trees, for efficient malware detection.

  • Explore anomaly detection techniques using botnet data and learn how to analyze network traffic for unusual patterns.

  • Collaborate and present research findings on current trends in network anomaly detection, enhancing communication and analytical skills.

Skills you'll gain

Category: Malware Analysis
Category: Research Presentation Skills
Category: Machine Learning for Detection
Category: Anomaly Detection Techniques
Category: Performance Evaluation

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

Category: Fraud Detection Techniques
Category: Adversarial Attack Implementation
Category: Model Evaluation and Optimization
Category: Generative Adversarial Networks (GANs)
Category: Reinforcement Learning Applications

Instructor

Lanier Watkins
Johns Hopkins University
3 Courses53 learners

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