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

被引：0
作者：
Han Y. [1 ]
Sun D. [1 ]
Li Y. [1 ]
机构：
[1] Department of Information Engineering, Shijiazhuang University of Applied Technology, Shijiazhuang
来源：
International Journal of Reasoning-based Intelligent Systems | 2024年 / 16卷 / 01期
关键词：
anonymity; convolutional neural network; internet of things; IoT; node privacy; protection method;
D O I：
10.1504/IJRIS.2024.137437
中图分类号：
学科分类号：
摘要：
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. Copyright © 2024 Inderscience Enterprises Ltd.
引用
收藏
页码：16 / 25
页数：9
相关论文
共 50 条
  • [21] Intrusion detection method based on a deep convolutional neural network
    Zhang S.
    Xie X.
    Xu Y.
    Qinghua Daxue Xuebao/Journal of Tsinghua University, 2019, 59 (01): : 44 - 52
  • [22] Method on Human Activity Recognition Based on Convolutional Neural Network
    Haibin, Zhang
    Kubota, Naoyuki
    INTELLIGENT ROBOTICS AND APPLICATIONS, ICIRA 2019, PT III, 2019, 11742 : 63 - 71
  • [23] Multifocus image fusion method based on a convolutional neural network
    Zhai, Hao
    Zhuang, Yi
    JOURNAL OF ELECTRONIC IMAGING, 2019, 28 (02)
  • [24] A novel method based on convolutional neural network for malaria diagnosis
    Hu, Junhua
    Liu, Jie
    Liang, Pei
    Li, Bo
    JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, 2020, 39 (05) : 7961 - 7976
  • [25] Graph Convolutional Neural Network Based Malware Detection in IoT-Cloud Environment
    Alsubaei, Faisal S.
    Alshahrani, Haya Mesfer
    Tarmissi, Khaled
    Motwakel, Abdelwahed
    INTELLIGENT AUTOMATION AND SOFT COMPUTING, 2023, 36 (03) : 2897 - 2914
  • [26] Intrusion Detection in IoT Systems Based on Deep Learning Using Convolutional Neural Network
    Pham Van Huong
    Le Duc Thuan
    Le Thi Hong Van
    Dang Viet Hung
    PROCEEDINGS OF 2019 6TH NATIONAL FOUNDATION FOR SCIENCE AND TECHNOLOGY DEVELOPMENT (NAFOSTED) CONFERENCE ON INFORMATION AND COMPUTER SCIENCE (NICS), 2019, : 448 - 453
  • [27] Defect detection method for fiber based on convolutional neural network
    Chen G.
    Yang Z.
    Zhongguo Guanxing Jishu Xuebao/Journal of Chinese Inertial Technology, 2019, 27 (01): : 95 - 100
  • [28] Development of Home Intelligent Fall Detection IoT System Based on Feedback Optical Flow Convolutional Neural Network
    Hsieh, Yi-Zeng
    Jeng, Yu-Lin
    IEEE ACCESS, 2018, 6 : 6048 - 6057
  • [29] A Forward Train Detection Method Based on Convolutional Neural Network
    Wang, Zhangyu
    Lee, Tony
    Leung, Michael
    Tang, Simon
    Zhang, Qiang
    Yang, Zining
    Cheung, Virginia
    INTELLIGENT HUMAN SYSTEMS INTEGRATION 2020, 2020, 1131 : 129 - 135
  • [30] A Pneumonia Detection Method Based on Improved Convolutional Neural Network
    Li, Xin
    Chen, Fan
    Hao, Haijiang
    Li, Mengting
    PROCEEDINGS OF 2020 IEEE 4TH INFORMATION TECHNOLOGY, NETWORKING, ELECTRONIC AND AUTOMATION CONTROL CONFERENCE (ITNEC 2020), 2020, : 488 - 493
12345