AI framework enhances IoT security with DRL and FL

Researchers have developed a novel AI framework to enhance security in the Internet of Things (IoT) by intelligently selecting suitable smart objects for service provisioning. The system utilizes Deep Reinforcement Learning (DRL) for adaptive service selection under security constraints and Federated Learning (FL) for distributed behavioral monitoring. This approach calculates a reliability score for service providers, ensuring that selection considers both functional suitability and adherence to security protocols, with experimental results showing its effectiveness even on resource-constrained IoT devices. AI

IMPACT This research offers a scalable security mechanism for modern IoT ecosystems, potentially improving the reliability and safety of connected devices.

RANK_REASON Academic paper detailing a new AI-based solution for a specific technical problem. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• Deep Reinforcement Learning
• Federated Learning
• Internet of Things
• Mert Cihangiroglu

• paper
• infra

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