Edge computing and AIoT based network intrusion detection mechanism

被引:2
作者
Sui, Qingru [1 ]
Liu, Xiaoyan [1 ]
机构
[1] Changchun Sci Tech Univ, Changchun 130600, Peoples R China
关键词
AIoT; edge computing; intrusion detection; machine learning;
D O I
10.1002/itl2.324
中图分类号
TN [电子技术、通信技术];
学科分类号
0809 ;
摘要
Edge computing technology solves the shortcomings of high latency, mobility and location awareness in remote cloud computing, but it also brings many security challenges to the AIoT. In view of the open and heterogeneous characteristics of edge network, a more secure edge computing intrusion detection method is studied in this paper. The main idea is to combine edge computing with AIoT to realize edge smart interconnection. This method optimizes the weight value in machine learning by increasing the screening and mitigation of cloud server training samples, so as to provide efficient and accurate edge intrusion detection behavior. The experimental results show that the proposed method can more effectively improve the detection accuracy and reduce the detection time.
引用
收藏
页数:5
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共 9 条
  • [1] Mobile Edge Computing: A Survey
    Abbas, Nasir
    Zhang, Yan
    Taherkordi, Amir
    Skeie, Tor
    [J]. IEEE INTERNET OF THINGS JOURNAL, 2018, 5 (01): : 450 - 465
  • [2] Smart innovative cities: The impact of Smart City policies on urban innovation
    Caragliu, Andrea
    Del Bo, Chiara F.
    [J]. TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE, 2019, 142 : 373 - 383
  • [3] Technology evolution from self-powered sensors to AIoT enabled smart homes
    Dong, Bowei
    Shi, Qiongfeng
    Yang, Yanqin
    Wen, Feng
    Zhang, Zixuan
    Lee, Chengkuo
    [J]. NANO ENERGY, 2021, 79
  • [4] A Deep Cycle Limit Learning Machine Method for Urban Expressway Traffic Incident Detection
    Fang, YunFeng
    Yang, Qingfang
    Zheng, Lili
    Zhou, Xiangyu
    Peng, Bo
    [J]. MATHEMATICAL PROBLEMS IN ENGINEERING, 2020, 2020
  • [5] An Adaptive Ensemble Machine Learning Model for Intrusion Detection
    Gao, Xianwei
    Shan, Chun
    Hu, Changzhen
    Niu, Zequn
    Liu, Zhen
    [J]. IEEE ACCESS, 2019, 7 : 82512 - 82521
  • [6] Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization
    Sharafaldin, Iman
    Lashkari, Arash Habibi
    Ghorbani, Ali A.
    [J]. ICISSP: PROCEEDINGS OF THE 4TH INTERNATIONAL CONFERENCE ON INFORMATION SYSTEMS SECURITY AND PRIVACY, 2018, : 108 - 116
  • [7] EMM: Energy-Aware Mobility Management for Mobile Edge Computing in Ultra Dense Networks
    Sun, Yuxuan
    Zhou, Sheng
    Xu, Jie
    [J]. IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, 2017, 35 (11) : 2637 - 2646
  • [8] Cloud Computing: a Perspective Study
    Wang, Lizhe
    von Laszewski, Gregor
    Younge, Andrew
    He, Xi
    Kunze, Marcel
    Tao, Jie
    Fu, Cheng
    [J]. NEW GENERATION COMPUTING, 2010, 28 (02) : 137 - 146
  • [9] Current advances and future challenges of AIoT applications in particulate matters (PM) monitoring and control
    Yang, Chao-Tung
    Chen, Ho-Wen
    Chang, En-Jui
    Kristiani, Endah
    Nguyen, Kieu Lan Phuong
    Chang, Jo-Shu
    [J]. JOURNAL OF HAZARDOUS MATERIALS, 2021, 419