Machine Learning and Security

Overview

Explore the intersection of machine learning and security with chess legend Garry Kasparov, delving into the implications for technology and society.

Syllabus

• Introduction to Machine Learning and Security
Overview of Machine Learning
Introduction to Cybersecurity
Intersection of ML and Security: Historical Context and Case Studies
• Fundamentals of Machine Learning
Types of Machine Learning: Supervised, Unsupervised, and Reinforcement Learning
Key Algorithms and Models: Decision Trees, Neural Networks, SVMs
Evaluation Metrics and Model Validation
• Cybersecurity Principles
Basic Concepts: Threats, Vulnerabilities, and Attack Vectors
Security Protocols and Standards
Risk Assessment and Management
• Threats to Machine Learning Systems
Adversarial Attacks on ML Models
Data Poisoning and Evasion Tactics
Model Inversion and Membership Inference Attacks
• Machine Learning for Security Applications
Network Intrusion Detection using ML
Anomaly Detection Techniques
Automated Threat Intelligence and Response
• Ethics and Privacy in Machine Learning and Security
Implications of AI in Surveillance and Privacy
Bias and Fairness in Security-Driven ML Models
Regulations and Compliance: GDPR, CCPA
• Case Studies and Real-World Applications
Analysis of High-Profile Security Breaches
Garry Kasparov's Insights on AI and Security
Lessons from Chess: Strategy and Adaptation in Cybersecurity
• Advanced Topics and Future Trends
Federated Learning and Data Privacy
Quantum Computing and its Impact on Security
AI-Driven Threat Hunting and Future of Security Operations
• Capstone Project
Design and Implement a Security Solution Using Machine Learning
Project Presentation and Class Discussion
• Conclusion and Future Directions
Recap and Key Takeaways
Future of AI in Security and Society
Q&A with Garry Kasparov: Perspectives on Technology and Strategy

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Subjects

Conference Talks