Title: Privacy protection and anomaly detection in intelligent sorting based on convolutional neural networks in IoT environment

Authors: Han Zhou; Danping Chen; Gengxin Chen; Xiaoli Lin

Addresses: College of Mechanical and Electrical Engineering, Hainan Vocational University of Science and Technology, Haikou, 571126, Hainan, China ' College of Mechanical and Electrical Engineering, Hainan Vocational University of Science and Technology, Haikou, 571126, Hainan, China ' College of Mechanical and Electrical Engineering, Hainan Vocational University of Science and Technology, Haikou, 571126, Hainan, China ' College of Mechanical and Electrical Engineering, Hainan Vocational University of Science and Technology, Haikou, 571126, Hainan, China

Abstract: At present, the Internet of Things (IoT) has improved people's lives. IoT provides users with various intelligent sorting, networked devices, and applications across different fields. Therefore, detecting anomalies in IoT devices with intelligent sorting is crucial to minimise threats and improve safety. The convolutional neural network-assisted anomaly detection (CNN-AD) method has been developed to enhance security by detecting anomalies in the IoT environment with intelligent sorting. The Anomaly detection method uses a focused event system to increase its efficiency in intelligent sorting with event grouping tasks and improve detection accuracy. The event privacy is obtained by utilising the feature selection, mapping, and normalisation to enhance security. CNN automatically extracts characteristics from data and identifies and classifies the different types of events and attacks in intelligent sorting. The performance analysis and assessments of CNN are based on detecting different classes of attacks and computation times that are significantly shorter.

Keywords: anomaly detection; CNN; convolutional neural network; classification; different attacks; privacy; security; intelligent sorting.

DOI: 10.1504/IJDS.2024.142820

International Journal of Data Science, 2024 Vol.9 No.3/4, pp.256 - 275

Received: 13 Jun 2024
Accepted: 06 Aug 2024
Published online: 23 Nov 2024 *

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