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AI framework enhances IoT security with DRL and FL

By PulseAugur Editorial · [1 sources] ·

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 →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

AI framework enhances IoT security with DRL and FL

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

  1. arXiv cs.AI TIER_1 English(EN) · Marco Arazzi, Mert Cihangiroglu, Serena Nicolazzo, Antonino Nocera, Vinod P ·

    An AI-Based Solution for Secure Service Provisioning in IoT

    arXiv:2606.30701v1 Announce Type: cross Abstract: As the Internet of Things (IoT) continues its rapid expansion, the attack surface grows accordingly, with emerging threats targeting smart objects and their interactions. In this evolving landscape, securing service provisioning i…

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