Enhancing IoT Security with Federated Learning and AI-driven Threat Detection

Abstract

The proliferation of interconnected devices within the Internet of Things (IoT) paradigm has introduced unprecedented challenges in ensuring robust security measures. As these devices generate vast amounts of data, their susceptibility to diverse cyber threats has escalated, demanding innovative security approaches. This paper presents an exploration into fortifying IoT security through the amalgamation of Federated Learning (FL) and Artificial Intelligence (AI)-driven threat detection mechanisms.

Federated Learning, a decentralized machine learning technique, is leveraged within the IoT ecosystem to enhance data privacy and mitigate concerns associated with centralized data storage. By enabling local model training on edge devices and aggregating insights without compromising sensitive information, FL serves as a foundation for secure collaborative learning among IoT nodes.

Additionally, this research introduces an AI-driven threat detection framework tailored for IoT environments. Employing machine learning algorithms, particularly deep learning models, facilitates the real-time identification and classification of potential threats, including intrusion attempts, malware, and anomalous behaviors. The integration of these AI-driven mechanisms with FL further fortifies the IoT ecosystem against evolving cyber threats.

Through comprehensive simulations and case studies, this paper demonstrates the efficacy and robustness of the proposed approach in bolstering IoT security. The findings showcase not only enhanced threat detection capabilities but also the preservation of data privacy and efficiency in resource-constrained IoT environments. This study contributes to the advancement of secure IoT systems by fostering collaborative learning while proactively detecting and mitigating potential security vulnerabilities.