I. INTRODUCTION  
A. IoT and Machine Learning  
B. IoT-integrated Machine Learning Security Risks  

II. ATTACK AND THREAT MODELS  
A. Actors in Threat Model  
B. Threats in ML-based IoT  
C. Categories of Attack Targets  
D. White-Box vs. Black-Box  
E. Passive vs. Active  
F. Training vs. Inference  
G. Adversary Model  

III. TAXONOMY OF ATTACKS IN ML-BASED IOT  
A. Membership Inference Attacks  
B. Adversarial Attacks  
C. Reconstruction Attacks  
D. Property Inference Attacks  
E. Model Extraction Attacks  
F. Poisoning Attacks  

IV. DISCUSSION  
A. Experimental Configurations  
B. Open Source Project Evaluation  
C. Adaptability to Different Hardware Configurations and Python Versions  
D. Suggestions and Explorations  

V. CONCLUSION