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课程大纲
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 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
要求
- An understanding of embedded systems or edge AI deployment environments
- Experience with Python and ML frameworks (e.g., TensorFlow Lite, PyTorch Mobile)
- Basic familiarity with cybersecurity or IoT threat models
Audience
- Embedded AI developers
- IoT security specialists
- Engineers deploying ML models on edge or constrained devices
14 小时
