Computer Science

Computer Science ›› 2024, Vol. 51 ›› Issue (11A): 240100137-9.doi: 10.11896/jsjkx.240100137

• Information Security • Previous Articles     Next Articles

IoT Devices Identification Method Based on Weighted Feature Fusion

CAO Weikang1,2, LIN Honggang1,2,3   

  1. 1 School of Cyber Security(Xin Gu Industrial College),Chengdu University of Information Technology,Chengdu 610225,China
    2 Sichuan Provincial Key Laboratory of Advanced Cryptography and System Security,Chengdu 610225,China
    3 Anhui Key Laboratory of Cyberspace Security Situational Awareness and Assessment,Hefei 230037,China
  • Online:2024-11-16 Published:2024-11-13
  • About author:CAO Weikang,born in 1999,postgraduate.His main research interests include cyberspace security and so on.
    LIN Honggang,born in 1976,Ph.D,professor.His main research interests include Internet of things security,network and system security
  • Supported by:
    National 242 Information Security Program project(2021-037) and Cyberspace Security Situation Awareness and Assessment,Anhui Province Key Laboratory(CSSAE-2021-002).

PDF (PC)

158

Abstract: IoT device identification plays an extremely important role in the field of device management and network security,which not only helps administrators review network assets in a timely manner,but also correlates device information with potential vulnerability information to discover potential security risks in a timely manner.The current IoT device identification methods do not make full use of the characteristics of iot devices,and it is difficult to identify devices with fewer samples in the case of unbalanced samples.To solve the above problems,this paper proposes a weighted feature fusion based method for IoT device recognition.A parallel structure of TextCNN-BiLSTM_Attention is designed to extract the local features and context features of the application layer service information of networked devices respectively.A weighted feature fusion algorithm is proposed to fuse the features extracted from different models.Finally,multi-layer perceptron is used to recognize the device.Experimental results show that the proposed method can extract the features of networked devices more comprehensively,identify devices with fewer samples under the condition of data imbalance,and the macro average precision rate is improved by 2.6%~12.85% compared with the existing methods,which has good characterization and generalization abilities,and is superior to multi-model methods such as CNN_LSTM in recognition efficiency.

Key words: Internet of Things, Equipment identification, TextCNN, BiLSTM_Attention, Feature extraction, Weighted feature fusion

CLC Number: 

  • TP393
[1]KOUICEM D E,BOUABDALLAH A,LAKHLEF H.Internet of things security:A top-down survey[J].Computer Networks,2018,141:199-221.
[2]KHAN M A,QUASIM M T,ALGARNI F,et al.Internet ofthings:On the opportunities,applications and open challenges in Saudi Arabia[C]//2019 International Conference on Advances in the Emerging Computing Technologies(AECT).IEEE,2020:1-5.
[3]ANTONAKAKIS M,APRIL T,BAILEY M,et al.Understanding the mirai botnet[C]//26th USENIX Security Symposium(USENIX Security 17).2017:1093-1110.
[4]FAN L,ZHANG S,WU Y,et al.An IoT device identification method based on semi-supervised learning[C]//2020 16th International Conference on Network and Service Management(CNSM).IEEE,2020:1-7.
[5]FENG X,LI Q,WANG H,et al.Acquisitional rule-based engine for discovering {Internet-of-Things} devices[C]//27th USENIX security symposium(USENIX Security 18).2018:327-341.
[6]FAN L N,LI C L,WU Y C,et al.Iot equipment identification and anomaly detection research review [J/OL].Journal of software,2024(1):288-308.https://doi.org/10.13328/j.cnki.jos.006818.
[7]CHUNG W H,GU Y H,YOO S J.CHP Engine Anomaly Detection Based on Parallel CNN-LSTM with Residual Blocks and Attention[J].Sensors,2023,23(21):8746.
[8]BREMLER-BARR A,LEVY H,YAKHINI Z.Iot or not:Identifying iot devices in a short time scale[C]//2020 IEEE/IFIP Network Operations and Management Symposium(NOMS 2020).IEEE,2020:1-9.
[9]REDONDO J M,CUESTA D.Towards improving productivity in nmap security audits[J].Journal of Web Engineering,2019,18(7):539-577.
[10]DURUMERIC Z,ADRIAN D,MIRAIN A,et al.A search engine backed by Internet-wide scanning[C]//Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security.2015:542-553.
[11]MIETTINEN M,MARCHAL S,HAFEEZ I,et al.Iot sentinel:Automated device-type identification for security enforcement in iot[C]//2017 IEEE 37th international conference on distributed computing systems(ICDCS).IEEE,2017:2177-2184.
[12]KOSTAS K,JUST M,LONES M A.IoTDevID:A behavior-based device identification method for the IoT[J].IEEE Internet of Things Journal,2022,9(23):23741-23749.
[13]SIVANATHAN A,SHERRATT D,GHARAKHEILI H H,et al.Characterizing and classifying IoT traffic in smart cities and campuses[C]//2017 IEEE Conference on Computer Communications Workshops(INFOCOM WKSHPS).IEEE,2017:559-564.
[14]LI R G,DUAN P Y,SHEN M,et al.Traffic classification algorithm for Internet of Things devices based on Random forest [J].Journal of Beijing University of Aeronautics and Astronautics,2022,48(2):233-239.
[15]ZHU B Y,CHEN X,SHA L T,et al.Two-layer iot device classification recognition Model based on traffic and text fingerprint [J].Computer Science,2023,50(8):304-313.
[16]LIU D D,HAN Y,LIU X Y,et al.Smart Home recognition method based on WiFi data frame feature [J].Computer Engineering and Applications,2023,59(15):274-280.
[17]YAO L,ZHUANG H,SU Q,et al.Automatic smart deviceidentification based on web fingerprint and neural network[C]//Proceedings of the 2021 3rd International Conference on Big-data Service and Intelligent Computation.2021:33-41.
[18]WAN S,LI Q,WANG H,et al.DevTag:A Benchmark for Fingerprinting IoT Devices[J].IEEE Internet of Things Journal,2022,10(7):6388-6399.
[19]CHEN Q G,DU Y H,HAN Y,et al.Iot device recognition mod-el based on deep separable Convolution [J].Information Network Security,201,21(9):67-73.
[20]YIN F,YANG L,WANG Y,et al.Iot etei:End-to-end iot device identification method[C]//2021 IEEE Conference on Dependable and Secure Computing(DSC).IEEE,2021:1-8.
[21]WANG Z H.Iot device identification method based on hybrid structure of LCNN and LSTM [J].Information Network Security,2023,23(6):43-54.
[22]TANG Y Z G,LU S D,QIAN L F,et al.IDFE:Fingerprint deep extraction method for iot device recognition [J/OL].Computer engineering and application:1-15.http://kns.cnki.net/kcms/detail/11.2127.tp.20230920.1333.054.html.
[23]DEVLIN J,CHANG M W,LEE K,et al.Bert:Pre-training of deep bidirectional transformers for language understanding[J].arXiv:1810.04805,2018.
[24]GREFF K,SRIVASTAVA R K,KOUTNÍK J,et al.LSTM:A search space odyssey[J].IEEE Transactions on Neural Networks and Learning Systems,2016,28(10):2222-2232.
[25]JOHNSON R,ZHANG T.Deep pyramid convolutional neuralnetworks for text categorization[C]//Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics(Volume 1:Long Papers).2017:562-570.
[1] XIAO Xiao, BAI Zhengyao, LI Zekai, LIU Xuheng, DU Jiajin. Parallel Multi-scale with Attention Mechanism for Point Cloud Upsampling [J]. Computer Science, 2024, 51(8): 183-191.
[2] CHEN Liang, LI Zhihua. Abnormal Traffic Detection Method for Multi-stage Attacks of Internet of Things Botnets [J]. Computer Science, 2024, 51(8): 379-386.
[3] ZANG Hongrui, YANG Tingting, LIU Hongbo, MA Kai. Study on Cryptographic Verification of Distributed Federated Learning for Internet of Things [J]. Computer Science, 2024, 51(6A): 230700217-5.
[4] YANG Pengyue, WANG Feng, WEI Wei. ConvNeXt Feature Extraction Study for Image Data [J]. Computer Science, 2024, 51(6A): 230500196-7.
[5] WANG Yanlin, SUN Jing, YANG Hongbo, GUO Tao, PAN Jiahua, WANG Weilian. Classification Model of Heart Sounds in Pulmonary Hypertension Based on Time-Frequency Fusion Features [J]. Computer Science, 2024, 51(6A): 230800091-7.
[6] TIAN Shuaihua, LI Zheng, WU Yonghao, LIU Yong. Identifying Coincidental Correct Test Cases Based on Machine Learning [J]. Computer Science, 2024, 51(6): 68-77.
[7] SUN Jing, WANG Xiaoxia. Convolutional Neural Network Model Compression Method Based on Cloud Edge Collaborative Subclass Distillation [J]. Computer Science, 2024, 51(5): 313-320.
[8] SONG Hao, MAO Kuanmin, ZHU Zhou. Algorithm of Stereo Matching Based on GAANET [J]. Computer Science, 2024, 51(4): 229-235.
[9] SHANG Yuling, LI Peng, ZHU Feng, WANG Ruchuan. Overview of IoT Traffic Attack Detection Technology Based on Fuzzy Logic [J]. Computer Science, 2024, 51(3): 3-13.
[10] WANG Wenmiao. Prediction of Lower Limb Joint Angle Based on VMD-ELMAN Electromyographic Signals [J]. Computer Science, 2024, 51(3): 257-264.
[11] ZHAO Jiangfeng, HE Hongjie, CHEN Fan, YANG Shubin. Two-stage Visible Watermark Removal Model Based on Global and Local Features for Document Images [J]. Computer Science, 2024, 51(2): 172-181.
[12] WANG Lina, LAI Kunhao, YANG Kang. CARINA:An Efficient Application Layer Protocol Conversion Approach for IoT Interoperability [J]. Computer Science, 2024, 51(2): 278-285.
[13] JING Youxian, ZHU Qingsheng. ORB Algorithm Based on Key Point Density Optimization [J]. Computer Science, 2024, 51(11A): 240300048-5.
[14] MA Junwei, PAN Xiukui, WANG Yuqi, WU Jian, DU Feng. Reliable Power Data Scheduling Scheme Based on Blockchain [J]. Computer Science, 2024, 51(11A): 231100178-8.
[15] ZHUO Mingsong, MO Lingfei. Spiking Neural Network Classification Model Based on Multi-subnetwork Pre-training [J]. Computer Science, 2024, 51(11A): 240300191-6.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
渝ICP备16013121号-4
Add:18# Honghu West Rd., Yubei ,Chongqing,China    Post Code: 401121
Tel: 023-63500828/023-67039612/023-67039623
E-mail: jsjkx12@163.com
Powered by Beijing Magtech Co., Ltd.