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A Neural Network-Based Trust Management System for Edge Devices in Peer-to-Peer Networks

Alanoud Alhussain1, Heba Kurdi1,*, Lina Altoaimy2

Computer Science Department, King Saud University, Riyadh, 11451, Saudi Arabia.
Information Technology Department, King Saud University, Riyadh, 11451, Saudi Arabia.

* Corresponding Author: Heba Kurdi. Email: .

Computers, Materials & Continua 2019, 59(3), 805-815.


Edge devices in Internet of Things (IoT) applications can form peers to communicate in peer-to-peer (P2P) networks over P2P protocols. Using P2P networks ensures scalability and removes the need for centralized management. However, due to the open nature of P2P networks, they often suffer from the existence of malicious peers, especially malicious peers that unite in groups to raise each other's ratings. This compromises users' safety and makes them lose their confidence about the files or services they are receiving. To address these challenges, we propose a neural network-based algorithm, which uses the advantages of a machine learning algorithm to identify whether or not a peer is malicious. In this paper, a neural network (NN) was chosen as the machine learning algorithm due to its efficiency in classification. The experiments showed that the NNTrust algorithm is more effective and has a higher potential of reducing the number of invalid files and increasing success rates than other well-known trust management systems.


Cite This Article

A. Alhussain, H. Kurdi and L. Altoaimy, "A neural network-based trust management system for edge devices in peer-to-peer networks," Computers, Materials & Continua, vol. 59, no.3, pp. 805–815, 2019.


This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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