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Course Outline

Fundamentals of Edge AI Security

  • Examining the key challenges in Edge AI security
  • Analyzing the threat landscape: cyber-attacks targeting edge devices
  • Navigating regulatory compliance and established security frameworks

Encryption and Authentication in Edge AI

  • Applying data encryption methods to protect AI models
  • Leveraging hardware-based security features like TPMs and secure enclaves
  • Establishing robust authentication and access control mechanisms

Securing AI Model Deployment and Integrity

  • Mitigating adversarial attacks on AI models
  • Implementing model obfuscation and protection techniques
  • Verifying model integrity and trustworthiness

Enhancing Resilience in Edge AI Systems

  • Creating fault-tolerant Edge AI architectures
  • Utilizing AI-driven anomaly detection for breach identification
  • Deploying automated threat response protocols

Secure Edge-to-Cloud Connectivity

  • Adopting secure communication protocols
  • Managing data privacy and federated learning in Edge AI
  • Maintaining adherence to industry security standards

Emerging Trends and Best Practices in Edge AI Security

  • Integrating AI-powered cybersecurity for edge computing
  • Preparing for emerging threats and evolving security strategies
  • Addressing ethical considerations in AI security

Recap and Future Directions

Requirements

  • Advanced proficiency in AI and machine learning principles
  • Practical experience with cybersecurity fundamentals and encryption protocols
  • Working knowledge of IoT and Edge computing landscapes

Target Audience

  • Cybersecurity experts
  • AI engineers
  • IoT developers
 21 Hours

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