Trust Score Prediction for IoT Device Onboarding Using Transfer and Few-Shot Learning in Consumer Electronics

The rapid proliferation of Internet of Things (IoT) devices in consumer electronics has made efficient trust score prediction essential for secure device onboarding. This paper presents a hybrid Trust Management framework that integrates few-shot and transfer learning with a statistical Markov chain foundation to address data scarcity and adaptability challenges in dynamic IoT environments. The few-shot learning phase enables rapid adaptation from minimal data (as few as 5–20 onboarding samples), while transfer learning ensures robust cross-domain generalizability (e.g., from consumer to industrial IoT). Comprehensive evaluation demonstrates that, with 20
onboarding samples, the proposed approach achieves fine-tuned Mean Squared Error (MSE) as low as 4.45 (XGBoost) and 5.07 (Random Forest), R2 scores exceeding 0.96, and average prediction error (MAE) below 1.55. Batched distributed ledger operations reduce total onboarding latency to under 750 ms for five devices, with system throughput averaging 137.7 devices per second across models. These results show the framework delivers accurate, low-latency, and scalable trust score predictions suitable for real-time onboarding in evolving IoT environments.