IoT individual privacy features analysis based on convolutional neural network

被引：6
|
作者：
Meng Xi [1 ]
Nie Lingyu [1 ]
Song Jiapeng [1 ]
机构：
[1] Chinese Peoples Publ Secur Univ, Sch Invest & Counter Terrorism, Beijing 100038, Peoples R China
来源：
COGNITIVE SYSTEMS RESEARCH | 2019年 / 57卷
关键词：
Convolutional neural network; Individual privacy; Global restriction; Protection algorithm; CLASSIFICATION; POLYMERS;
D O I：
10.1016/j.cogsys.2018.09.031
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In terms of the evaluation problems about individual privacy defense situation of cyberspace, an evaluation method for individual privacy security defense situation based on convolutional neural network is proposed in this thesis; firstly, the state of situation factor at different times in the individual privacy defense system is subject to fuzzy and probabilistic processing based on Bayesian algorithm, to build up situation awareness and situation estimation model and then input the initial condition probability, state transition probability and observation data into the model; secondly, the convolutional neural network is introduced to recognize and evaluate the Bayesian algorithm model mentioned above, improving the accuracy of global restriction protection algorithm for individual privacy features; finally, simulation experiment is carried out to verify the performance advantages of modeling of the algorithm mentioned above. (C) 2018 Elsevier B.V. All rights reserved.
引用
收藏
页码：126 / 130
页数：5
相关论文
共 50 条
  • [21] Privacy Preserving Inference with Convolutional Neural Network Ensemble
    Xiong, Alexander
    Nguyen, Michael
    So, Andrew
    Chen, Tingting
    2020 IEEE 39TH INTERNATIONAL PERFORMANCE COMPUTING AND COMMUNICATIONS CONFERENCE (IPCCC), 2020,
  • [22] Deep convolutional neural network and IoT technology for healthcare
    Wassan, Sobia
    Dongyan, Hu
    Suhail, Beenish
    Jhanjhi, N. Z.
    Xiao, Guanghua
    Ahmed, Suhail
    Murugesan, Raja Kumar
    DIGITAL HEALTH, 2024, 10
  • [23] Optimization Assisted Convolutional Neural Network for Sentiment Analysis with Weighted Holoentropy-based Features
    Krishnan, Hema
    Elayidom, M. Sudheep
    Santhanakrishnan, T.
    INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING, 2021, 20 (04) : 1261 - 1297
  • [24] Convolutional Neural Network Features Based Change Detection in Satellite Images
    El Amin, Arabi Mohammed
    Liu, Qingjie
    Wang, Yunhong
    FIRST INTERNATIONAL WORKSHOP ON PATTERN RECOGNITION, 2016, 0011
  • [25] Lesion Detection of Endoscopy Images Based on Convolutional Neural Network Features
    Zhu, Rongsheng
    Zhang, Rong
    Xue, Dixiu
    2015 8TH INTERNATIONAL CONGRESS ON IMAGE AND SIGNAL PROCESSING (CISP), 2015, : 372 - 376
  • [26] Multidimensional analysis and prediction based on convolutional neural network
    Bao, Jie
    SOFT COMPUTING, 2023,
  • [27] On the Location Dependence of Convolutional Neural Network Features
    Workman, Scott
    Jacobs, Nathan
    2015 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION WORKSHOPS (CVPRW), 2015,
  • [28] Amur tiger stripes: individual identification based on deep convolutional neural network
    Shi, Chunmei
    Liu, Dan
    Cui, Yonglu
    Xie, Jiajun
    Roberts, Nathan James
    Jiang, Guangshun
    INTEGRATIVE ZOOLOGY, 2020, 15 (06): : 461 - 470
  • [29] 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
  • [30] 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
12345