Sleeping with One AI Open

Overview

Explore adversarial attacks on AI/ML systems, their real-world implications, and mitigation strategies. Learn about potential risks and security considerations in AI-driven technologies across various sectors.

Syllabus

• Introduction to Adversarial Attacks
Definition and Overview of Adversarial Attacks
Historical Context and Evolution
Key Terminologies and Concepts
• Types of Adversarial Attacks
Evasion Attacks
Poisoning Attacks
Model Stealing Attacks
Inference Attacks
• Adversarial Attacks in AI/ML Systems
Impact on Image Recognition Models
Vulnerabilities in Natural Language Processing
Challenges in Autonomous Systems
Adversarial Examples in Reinforcement Learning
• Case Studies of Adversarial Attacks
High-Profile Incidents and Analysis
Sector-specific Vulnerabilities
Lessons Learned and Best Practices
• Real-World Implications and Risks
Ethical Considerations and Accountability
Economic Impact on Businesses
Privacy and Security Concerns
Regulatory and Legal Aspects
• Mitigation Strategies for Adversarial Attacks
Robustness and Defense Techniques
Adversarial Training Models
Monitoring and Detection Systems
Use of AI for Threat Intelligence
• Security Considerations in AI-Driven Technologies
Best Practices for AI Security
Designing Secure AI Systems
Assessing and Managing Risk in AI Deployments
• Sector-Specific Security Challenges
Healthcare
Finance
Automotive and Transportation
Telecommunications
• Future Trends and Research Directions
Emerging Threats in AI Security
Innovations in Defense Mechanisms
Role of AI Ethics in Security Research
• Course Summary and Final Assessment
Recap of Key Concepts
Final Project/Assessment
Resources for Continued Learning

Subjects

Conference Talks