AI Academy

Introduction to AI for Cybersecurity

Instructor: Lanier Watkins Duration: 9 hours to complete Recommended experience
Objective 1 Use AI techniques to detect and mitigate various cyber threats, protecting digital assets and data.
Objective 2 Develop and apply machine learning models to identify, classify, and filter spam and phishing emails.
Objective 3 Implement AI-driven biometric solutions like keystroke dynamics and facial recognition to enhance user authentication security.
Cybersecurity
Machine Learning Algorithms
Machine Learning
Authentications
Natural language processing
Computer Vision
Artificial Intelligence and Machine Learning (AI/ML)
Email Security
Cyber Threat Intelligence
Cyber Attacks
Multi-Factor Authentication
Artificial Intelligence
Intrusion Detection and Prevention
Threat Modeling
Threat Detection
Deep Learning
Jupyter

Advanced Malware and Network Anomaly Detection

Instructor: Lanier Watkins Duration: 11 hours to complete 3 weeks at 3 hours a week
Objective 1 Understand various types of malware and apply foundational analysis techniques to effectively detect and classify them.
Objective 2 Implement advanced machine learning algorithms, including clustering and decision trees, for efficient malware detection.
Objective 3 Explore anomaly detection techniques using botnet data and learn how to analyze network traffic for unusual patterns.
Objective 4 Collaborate and present research findings on current trends in network anomaly detection, enhancing communication and analytical skills.
Supervised Learning
Threat Detection
Machine Learning Methods
Network Analysis
System Design and Implementation
Network Security
Malware Protection
Cybersecurity
Machine Learning Algorithms
Intrusion Detection and Prevention
Machine Learning
Continuous Monitoring
Machine Learning Software
Anomaly Detection
Microsoft Windows
Performance Testing

Securing AI and Advanced Topics

Instructor: Lanier Watkins Duration: 2 weeks to complete at 10 hours a week
Objective 1 Learn to implement AI-based solutions to detect and prevent credit card fraud in cloud environments.
Objective 2 Explore the fundamentals of Generative Adversarial Networks and their applications in generating synthetic data.
Objective 3 Gain hands-on experience with black-box and white-box adversarial attacks to assess and enhance model resilience.
Objective 4 Master techniques in feature engineering and performance evaluation to optimize AI models for cybersecurity applications.
Machine Learning
Deep Learning
Threat Detection
Feature Engineering
Generative AI
Reinforcement Learning
Security Testing
Cloud Solutions
Cybersecurity
Artificial Intelligence
Cyber Threat Intelligence
Anomaly Detection
Artificial Intelligence and Machine Learning (AI/ML)
Performance Tuning