Machine-Learning Health Monitoring System for Resource-Scarce Embedded Systems

Machine-Learning model implementation in Resource-Scarce Embedded Systems is becoming a standard in many systems and projects. This implementation allows systems to be less Cloud dependent and make decisions independently. As these systems’ reasoning becomes intricate with the Machine Learning model’s decision, an attack to change the Machine Learning model’s data structure can make the entire system misbehave, which in some solutions can be critical. Therefore, it is necessary to create low-overhead tools to flag any miscalculation or wrongdoing during the model’s inference phase. The following work presents a Machine Learning Health Monitoring system based on PCA and Control Charts to verify if the model’s inference function runs properly. The solution presents a reasonable flag rate and is implemented e ciently in a Resource-Scarce Embedded System due to its reduced memory and processing footprints.