Hybrid Deep Learning and Distrust Model for Fault Detection in IoT Networks

Manh Hung Nguyen

Abstract: The proliferation of the Internet of Things (IoT) has led to an unprecedented integration of diverse sensors, driving innovation across numerous domains. However, the reliability and security of IoT networks are significantly challenged by the presence of faulty sensors. Traditional fault detection methods are inadequate to manage the scale and complexity of modern IoT environments. This paper addresses the challenge of identifying faulty sensors in large-scale IoT networks by proposing a hybrid fault detection model that integrates deep learning and distrust mechanisms. Tested on simulated Hanoi air pollution data, the model demonstrates high accuracy and effectiveness, surpassing traditional fault detection methods. This approach provides a scalable, efficient solution to enhance the reliability of IoT networks.

Keywords: IoT network, faulty sensor detection, deep learning, distrust model, anomaly detection

How to Cite?: Manh Hung Nguyen, "Hybrid Deep Learning and Distrust Model for Fault Detection in IoT Networks", Volume 13 Issue 11, November 2024, International Journal of Science and Research (IJSR), Pages: 166-170, https://www.ijsr.net/getabstract.php?paperid=SR241030132810, DOI: https://dx.doi.org/10.21275/SR241030132810

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