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Course Outline
Introduction to Edge AI and Embedded Systems
- What is Edge AI? Use cases and constraints.
- Edge hardware platforms and software stacks.
- Security challenges in embedded and decentralized environments.
Threat Landscape for Edge AI
- Physical access and tampering risks.
- Adversarial examples and model manipulation.
- Data leakage and model inversion threats.
Securing the Model
- Model hardening and quantization strategies.
- Watermarking and fingerprinting models.
- Defensive distillation and pruning.
Encrypted Inference and Secure Execution
- Trusted execution environments (TEEs) for AI.
- Secure enclaves and confidential computing.
- Encrypted inference using homomorphic encryption or secure multi-party computation (SMPC).
Tamper Detection and Device-Level Controls
- Secure boot and firmware integrity checks.
- Sensor validation and anomaly detection.
- Remote attestation and device health monitoring.
Edge-to-Cloud Security Integration
- Secure data transmission and key management.
- End-to-end encryption and data lifecycle protection.
- Cloud AI orchestration with edge security constraints.
Best Practices and Risk Mitigation Strategy
- Threat modeling for edge AI systems.
- Security design principles for embedded intelligence.
- Incident response and firmware update management.
Summary and Next Steps
Requirements
- A foundational understanding of embedded systems or edge AI deployment environments.
- Experience with Python and machine learning frameworks (e.g., TensorFlow Lite, PyTorch Mobile).
- Basic familiarity with cybersecurity concepts or IoT threat models.
Target Audience
- Embedded AI developers.
- IoT security specialists.
- Engineers deploying machine learning models on edge or constrained devices.
14 Hours
Testimonials (1)
The profesional knolage and the way how he presented it before us
