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

被引：0

作者：

Zhou, Han ^{[1
]}

Chen, Danping ^{[1
]}

Chen, Gengxin ^{[1
]}

Lin, Xiaoli ^{[1
]}

机构：

[1] College of Mechanical and Electrical Engineering, Hainan Vocational University of Science and Technology, Hainan, Haikou,571126, China

来源：

International Journal of Data Science | 2024年 / 9卷 / 3-4期

关键词：

Convolutional neural networks;

D O I：

10.1504/IJDS.2024.142820

中图分类号：

学科分类号：

摘要：

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. Copyright © 2024 Inderscience Enterprises Ltd.

引用

页码：256 / 275

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