Volume 50 Issue 3
Mar.  2024
Turn off MathJax
Article Contents
Article Navigation >  Journal of Mine Automation  > 2024  >  50(3): 99-107
HAO Qinxia, LI Huimin. Recognition model of IIoT equipment in coal mine[J]. Journal of Mine Automation，2024，50（3）：99-107.  doi: 10.13272/j.issn.1671-251x.2023100092
Citation: HAO Qinxia, LI Huimin. Recognition model of IIoT equipment in coal mine[J]. Journal of Mine Automation，2024，50（3）：99-107.  doi: 10.13272/j.issn.1671-251x.2023100092
shu

Recognition model of IIoT equipment in coal mine

doi: 10.13272/j.issn.1671-251x.2023100092

  • College of Communication and Information Engineering, Xi'an University of Science and Technology, Xi'an 710054, China

  • Received Date: 2023-10-30
  • Rev Recd Date: 2024-03-23
    • Available Online: 2024-04-11
    Abstract
  • The computing and storage resources of the industrial Internet of things (IIoT) equipment in the coal mine are limited, making it vulnerable to illegal network intrusion, causing sensitive data leakage or malicious tampering, and threatening the safety of coal mine production. Precise recognition of coal mine IIoT equipment can achieve effective management and maintenance of equipment operation, improve equipment safety and protection capabilities. However, existing equipment recognition algorithms suffer from complex feature construction, high memory and computing requirements, making it difficult to deploy in resource limited coal mine IIoT equipment. In order to solve the above problems, a coal mine IIoT equipment recognition model is proposed. Firstly, the model performs traffic segmentation, irrelevant field removal, deduplication, and fixed length field truncation operations on traffic data that supports TCP/IP protocol transmission. The model then converts it to IDX format for storage. Secondly, the model uses convolutional block attention module (CBAM) to optimize depthwise separable convolu-tion(DSC). A lightweight DSC-CBAM model is constructed to filter Non-IIoT equipment. Thirdly, the Wasserstein generative adversarial network with gradient penalty (WGAN-GP) is used to expand the data of coal mine IIoT equipment with less traffic, achieving the goal of balancing offset traffic data. Finally, multi-scale feature fusion (MFF) technology is introduced on the basis of DSC-CBAM to capture shallow global feature information, and Mish activation function is added to improve model training stability. The MFF-DSC-CBAM-Mish (MDCM) model is established to achieve precise recognition of coal mine IIoT equipment. The experimental results show that the model has a fast convergence speed, with accuracy, recall, precision, and F1 score all reaching 99.98%. The model has a small number of parameters, which can accurately and efficiently recognize IIoT equipment in coal mines.

     

    • FullText(HTML)
  • loading
    • References(34)
  • [1]
    李首滨,刘道园. 基于工业互联网的智能矿山研究与设计[J]. 中国煤炭,2021,47(2):59-65.

    LI Shoubin,LIU Daoyuan. Research and design of intelligent mine based on industrial Internet[J]. China Coal,2021,47(2):59-65.
    [2]
    HAO Qinxia,RONG Zheng. IoTTFID:an incremental IoT device identification model based on traffic fingerprint[J]. IEEE Access,2023,11:58679-58691. doi: 10.1109/ACCESS.2023.3284542
    [3]
    LIU Jie,SUN Yi,XU Fengkai,et al. IIS:intelligent identification scheme of massive IoT devices[C]. IEEE 45th Annual Computers,Software,and Applications Conference,Madrid,2021:1623-1626.
    [4]
    ZHOU Feng,QU Hua,LIU Hailong,et al. Fingerprinting IIoT devices through machine learning techniques[J]. Journal of Signal Processing Systems,2021,93:779-794. doi: 10.1007/s11265-021-01656-0
    [5]
    MIETTINEN M,MARCHAL S,HAFEEZ I,et al. IoT sentinel:automated device-type identification for security enforcement in IoT[C]. IEEE 37th International Conference on Distributed Computing Systems,Atlanta,2017:2177-2184.
    [6]
    SIVANATHAN A,GHARAKHEILI H H,LOI F,et al. Classifying IoT devices in smart environments using network traffic characteristics[J]. IEEE Transactions on Mobile Computing,2018,18(8):1745-1759.
    [7]
    MAHESH B. Machine learning algorithms-a review[J]. International Journal of Science and Research,2020,9(1):381-386.
    [8]
    ALZUBAIDI L,ZHANG Jinglan,HUMAIDI A J,et al. Review of deep learning:concepts,CNN architectures,challenges,applications,future directions[J]. Journal of Big Data,2021,8:1-74. doi: 10.1186/s40537-020-00387-6
    [9]
    ORTIZ J,CRAWFORD C,LE F. DeviceMien:network device behavior modeling for identifying unknown IoT devices[C]. International Conference on Internet of Things Design and Implementation,Montreal,2019:106-117.
    [10]
    YIN Feihong,YANG Li,WANG Yuchen,et al. IoT ETEI:end-to-end IoT device identification method[C]. IEEE Conference on Dependable and Secure Computing,Aizuwakamatsu,2021:1-8.
    [11]
    KOTAK J,ELOVICI Y. IoT device identification based on network communication analysis using deep learning[J]. Journal of Ambient Intelligence and Humanized Computing,2023,14(7):9113-9129. doi: 10.1007/s12652-022-04415-6
    [12]
    KRIZHEVSKY A,SUTSKEVER I,HINTON G E. ImageNet classification with deep convolutional neural networks[J]. Advances in neural information processing systems,2012,25(2). DOI: 10.1145/3065386.
    [13]
    HOWARD A,SANDLER M,CHEN Bo,et al. Searching for mobileNetV3[C]. IEEE/CVF International Conference on Computer Vision,Seoul,2019:1314-1324.
    [14]
    ELSKEN T,METZEN J H,HUTTER F. Neural architecture search:a survey[J]. Journal of Machine Learning Research,2019,20(55):1-21.
    [15]
    HAN Song,MAO Huizi,DALLY W J. Deep compression:compressing deep neural networks with pruning,trained quantization and Huffman coding[J]. Fiber,2015,56(4):3-7.
    [16]
    HAN Song,POOL J,TRAN J,et al. Learning both weights and connections for efficient neural network[J]. Advances in Neural Information Processing Systems,2015. DOI: 10.48550/arXiv.1506.02626.
    [17]
    王军,冯孙铖,程勇. 深度学习的轻量化神经网络结构研究综述[J]. 计算机工程,2021,47(8):1-13.

    WANG Jun,FENG Suncheng,CHENG Yong. Survey of research on lightweight neural network structures for deep learning[J]. Computer Engineering,2021,47(8):1-13.
    [18]
    WOO S,PARK J,LEE J Y,et al. CBAM:convolutional block attention module[C]. European Conference on Computer Vision,Munich,2018:3-19.
    [19]
    GULRAJANI I,AHMED F,ARJOVSKY M,et al. Improved training of Wasserstein GANs[J]. Advances in Neural Information Processing Systems,2017. DOI: 10.48550/arXiv.1704.00028.
    [20]
    DUBEY S R,SINGH S K,CHAUDHURI B B. Activation functions in deep learning:a comprehensive survey and benchmark[J]. Neurocomputing,2022,503:92-108. doi: 10.1016/j.neucom.2022.06.111
    [21]
    WANG Wei,ZHU Ming,WANG Jinlin,et al. End-to-end encrypted traffic classification with one-dimensional convolution neural networks[C]. IEEE International Conference on Intelligence and Security Informatics,Beijing,2017:43-48.
    [22]
    王伟. 基于深度学习的网络流量分类及异常检测方法研究[D]. 合肥:中国科学技术大学,2018.

    WANG Wei. Deep learning for network traffic classification and anomaly detection[D]. Hefei:University of Science and Technology of China,2018.
    [23]
    WANG Wei,ZHU Ming,ZENG Xuewen,et al. Malware traffic classification using convolutional neural network for representation learning[C]. International Conference on Information Networking,Da Nang,2017:712-717.
    [24]
    KOUKIS D,ANTONATOS S,ANTONIADES D,et al. A generic anonymization framework for network traffic[C]. IEEE International Conference on Communications,Istanbul,2006:2302-2309.
    [25]
    WANG Wei,SHENG Yiqiang,WANG Jinlin,et al. HAST-IDS:learning hierarchical spatial-temporal features using deep neural networks to improve intrusion detection[J]. IEEE Access,2017,6:1792-1806.
    [26]
    FAN Linna,HE Lin,DONG Enhuan,et al. EvoIoT:an evolutionary IoT and non-IoT classification model in open environments[J]. Computer Networks,2022,219. DOI: 10.1016/j.comnet.2022.109450.
    [27]
    孙洁娣,王利轩,温江涛,等. 改进稠密块轻量化神经网络的管道泄漏孔径识别[J]. 仪器仪表学报,2022,43(3):98-108.

    SUN Jiedi,WANG Lixuan,WEN Jiangtao,et al. Pipeline leakage aperture recognition based on lightweight neural network with the improved dense block[J]. Chinese Journal of Scientific Instrument,2022,43(3):98-108.
    [28]
    HU Jie,SHEN Li,SUN Gang. Squeeze-and-excitation networks[C]. IEEE/CVF Conference on Computer Vision and Pattern Recognition,Salt Lake City,2018:7132-7141.
    [29]
    WANG Qilong,WU Banggu,ZHU Pengfei,et al. ECA-Net:efficient channel attention for deep convolutional neural networks[C]. IEEE/CVF Conference on Computer Vision and Pattern Recognition,Seattle,2020:11534-11542.
    [30]
    HU Jie,SHEN Li,ALBANIE S,et al. Gather-excite:exploiting feature context in convolutional neural networks[J]. Advances in Neural Information Processing Systems,2018. DOI: 10.48550/arXiv.1810.12348.
    [31]
    LI Xiang,WANG Wenhai,HU Xiaolin,et al. Selective kernel networks[C]. IEEE/CVF Conference on Computer Vision and Pattern Recognition,Long Beach,2019:510-519.
    [32]
    SANDLER M,HOWARD A,ZHU Menglong,et al. MobileNetV2:inverted residuals and linear bottlenecks[C]. IEEE Conference on Computer Vision and Pattern Recognition,Salt Lake City,2018:4510-4520.
    [33]
    THOUTI S,VENU N,RINKU D R,et al. Investigation on identify the multiple issues in IoT devices using convolutional neural network[J]. Measurement:Sensors,2022. DOI: 10.1016/j.measen.2022.100509.
    [34]
    何志敏. 基于网络流量的物联网设备识别技术研究[D]. 南京:南京邮电大学,2022.

    HE Zhimin. Research on IoT device identification method based on network traffic[D]. Nanjing:Nanjing University of Posts and Telecommunications,2022.
    • Relative Articles
    • Supplements(0)
    • Cited By
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(6)  / Tables(4)

    Get Citation
    PDF
    XML

    Article Metrics

    Article views (103) PDF downloads(25) Cited by()
    Proportional views
    Related

    苏ICP备 05016349号-2

    Supported by: Beijing Renhe Information Technology Co. Ltd.  Visits：    

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return