Title: A privacy protection method for IoT nodes based on convolutional neural network

Authors: Yuexia Han; Di Sun; Yanjing Li

Addresses: Department of Information Engineering, Shijiazhuang University of Applied Technology, Shijiazhuang 050081, China ' Department of Information Engineering, Shijiazhuang University of Applied Technology, Shijiazhuang 050081, China ' Department of Information Engineering, Shijiazhuang University of Applied Technology, Shijiazhuang 050081, China

Abstract: In order to improve the security of internet of things, a privacy protection method of internet of things nodes based on convolutional neural network is proposed. Firstly, the flow model of IoT network nodes is constructed while using the ant colony algorithm to solve the model to obtain the current flow data of IoT nodes. Secondly, a convolutional neural network model is established to identify abnormal nodes in the internet of things. Finally, the privacy protection strategy of k-anonymous IoT nodes based on the average degree of nodes is adopted to protect the privacy of IoT abnormal nodes. The experimental results show that the method can effectively extract the node traffic before and after the attack on the internet of things, and the deviation value is only 2 kb/s; the identification results are more accurate, and the privacy of the internet of things nodes can be effectively protected.

Keywords: convolutional neural network; internet of things; IoT; node privacy; protection method; anonymity.

DOI: 10.1504/IJRIS.2024.137437

International Journal of Reasoning-based Intelligent Systems, 2024 Vol.16 No.1, pp.16 - 25

Received: 22 Sep 2022
Accepted: 08 Nov 2022
Published online: 19 Mar 2024 *