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A neural network approach for wireless spectrum anomaly detection in 5G-unlicensed network

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Abstract

The 5G New Radio Unlicensed (5G-U) technology has enabled manufacturing enterprises to deploy their own private industrial networks, making anomaly event detection more necessary for maintaining wireless communication quality. However, existing statistical analysis algorithms cannot efficiently and accurately detect various kinds of anomaly events caused by the complex industrial environment. These events include electromagnetic interference as well as contention between cross-technology devices for unlicensed spectrum resources. To improve the efficiency, we design a classification algorithm that uses feature extraction in the frequency domain and a convolutional neural network model to detect various kinds of anomaly events (e.g., loose antennas and co-channel interference). We prototyped Slade (Spectrum Learning for Anomaly Detection), an anomaly detection system for industrial 5G networks. To evaluate the system, we collect wireless spectrum data with two industrial 5G-U terminals. Our evaluation on the dataset shows that our methodology can accurately detect different anomaly events, with an accuracy of 97.6% and a recall of 97.1%.

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References

  • Bajracharya R, Shrestha R, Jung H (2020) Future isunlicensed: Private 5g unlicensed network for con-necting industries of future. Sensors 20(10), https://doi.org/10.3390/s20102774, https://www.mdpi.com/1424-8220/20/10/2774

    Article  Google Scholar 

  • Chandola V, Banerjee A, Kumar V (2009) Anomalydetection: A survey. ACM Comput Surv 41. https://doi.org/10.1145/1541880.1541882

  • Cheng Q, Shi Z, Nguyen DN, Dutkiewicz E (2019) Deeplearning network based spectrum sensing methods for ofdm systems. 1807.09414

  • Digham, F.F., Alouini, M.S., Simon, M.K.: On the energy detection of unknown signals over fading channels. IEEE Trans. Commun. 55(1), 21–24 (2007). https://doi.org/10.1109/TCOMM.2006.887483

    Article  Google Scholar 

  • Feng, Q., Zhang, Y., Li, C., Dou, Z., Wang, J.: Anomaly detection of spectrum in wireless communication via deep auto-encoders. J. Supercomput. 73, 3161–3178 (2017)

    Article  Google Scholar 

  • Hasan, M., Islam, M.M., Zarif, M.I.I., Hashem, M.: Attack and anomaly detection in iot sensors in iot sites using machine learning approaches. Internet Things 7, 100059 (2019)

    Article  Google Scholar 

  • Lee, W., Kim, M., Cho, D.H.: Deep cooperative sensing: Cooperative spectrum sensing based on convolutional neural networks. IEEE Trans. Veh. Technol. 68(3), 3005–3009 (2019). https://doi.org/10.1109/TVT.2019.2891291

    Article  Google Scholar 

  • Lees, W.M., Wunderlich, A., Jeavons, P.J., Hale, P.D., Souryal, M.R.: Deep learning classification of 3.5-ghz band spectrograms with applications to spectrum sensing. IEEE Trans. Cogn. Commun. Netw. 5(2), 224–236 (2019). https://doi.org/10.1109/TCCN.2019.2899871

    Article  Google Scholar 

  • Li, Z., Chen, W., Pei, D.: Robust and unsupervised kpi anomaly detection based on conditional variational autoencoder. In: 2018 IEEE 37th International Performance Computing and Communications Conference (IPCCC), pp. 1–9 (2018). https://doi.org/10.1109/PCCC.2018.8710885

  • Liu, D., Zhao, Y., Xu, H., Sun, Y., Pei, D., Luo, J., Jing, X., Feng, M.: Opprentice: towards practical and automatic anomaly detection through machine learning. In: Proceedings of the 2015 Internet Measurement Conference (2015)

  • Lunden, J., Kassam, S.A., Koivunen, V.: Robust nonparametric cyclic correlation-based spectrum sensing for cognitive radio. IEEE Trans. Signal Process. 58(1), 38–52 (2010). https://doi.org/10.1109/TSP.2009.2029790

    Article  MathSciNet  MATH  Google Scholar 

  • Luo, Z., Wang, W., Qu, J., Jiang, T., Zhang, Q.: Shieldscatter: Improving iot security with backscatter assistance. In: Proceedings of the 16th ACM conference on embedded networked sensor systems (2018)

  • Lu, X., Petrov, V., Moltchanov, D., Andreev, S., Mahmoodi, T., Dohler, M.: 5G-U: Conceptualizing Integrated Utilization of Licensed and Unlicensed Spectrum for Future IoT. IEEE Commun. Mag. 57(7), 92-98 (2019). https://doi.org/10.1109/MCOM.2019.1800663

    Article  Google Scholar 

  • Naik, G., Park, J.M., Ashdown, J., Lehr, W.: Next Generation Wi-Fi and 5G NR-U in the 6 GHz Bands: Opportunities and Challenges. IEEE Access 8 153027–153056 (2020). https://doi.org/10.1109/ACCESS.2020.3016036

    Article  Google Scholar 

  • Nedelkoski, S., Cardoso, J. and Kao, O.: Anomaly detection from system tracing data using multimodal deep learning. In: 2019 IEEE 12th International Conference on Cloud Computing (CLOUD), IEEE, pp 179–186 (2019)

  • O’Shea, T., Clancy, T., McGwier, R.: Recurrent neural radio anomaly detection. arXiv:abs/1611.00301 (2016)

  • Pei-Han, Q., Zan, L., Jiang-Bo, S., Rui, G.: A robust power spectrum split cancellation-based spectrum sensing method for cognitive radio systems. Chin. Phys. B 23, 5 (2014). https://doi.org/10.1088/1674-1056/23/12/128401

    Article  Google Scholar 

  • Rajendran, S., Meert, W., Lenders, V., Pollin, S.: Saife: Unsupervised wireless spectrum anomaly detection with interpretable features. In: 2018 IEEE international symposium on dynamic spectrum access networks (DySPAN), pp. 1–9. https://doi.org/10.1109/DySPAN.2018.8610471 (2018)

  • Siripanadorn, S., Hattagam, W., Teaumroong, N.: Anomaly detection using self-organizing map and wavelets in wireless. Sensor Netw. 4, 291–297 (2010)

    Google Scholar 

  • Sun, D., Lu, S., Wang, W.: Caae: A novel wireless spectrum anomaly detection method with multiple scoring criterion. In: 2021 28th International Conference on Telecommunications (ICT), pp 1–5 (2021). https://doi.org/10.1109/ICT52184.2021.9511462

  • Tandiya, N., Jauhar, A., Marojevic, V., Reed, J.H.: Deep predictive coding neural network for rf anomaly detection in wireless networks. In: 2018 IEEE International Conference on Communications Workshops (ICC Workshops), pp 1–6 (2018). https://doi.org/10.1109/ICCW.2018.8403654

  • Takianngam, S., Usaha, W.: Discrete wavelet transform and one-class support vector machines for anomaly detection in wireless sensor networks. In: 2011 international symposium on intelligent signal processing and communications systems (ISPACS), pp. 1–6. https://doi.org/10.1109/ISPACS.2011.6146181 (2011)

  • Theissler, A.: Detecting known and unknown faults in automotive systems using ensemble-based anomaly detection. Knowl.-Based Syst. 123, 163–173 (2017). https://doi.org/10.1016/j.knosys.2017.02.023

    Article  Google Scholar 

  • Wang, Y., Wang, Z., Xie, Z., Zhao, N., Chen, J., Zhang, W., Sui, K., Pei, D.: Practical and white-box anomaly detection through unsupervised and active learning. In: 2020 29th international conference on computer communications and networks (ICCCN), pp. 1–9. https://doi.org/10.1109/ICCCN49398.2020.9209704 (2020)

  • Wang, B., Tao, F., Fang, X., Liu, C., Liu, Y., Freiheit, T.: Smart manufacturing and intelligent manufacturing: a comparative review. Engineering 7(6), 738–757 (2021). https://doi.org/10.1016/j.eng.2020.07.017

    Article  Google Scholar 

  • Xu, H., Chen, W., Zhao, N., Li, Z., Bu, J., Li, Z., Liu, Y., Zhao, Y., Pei, D., Feng, Y., Chen, J., Wang, Z., Qiao, H.: Unsupervised anomaly detection via variational auto-encoder for seasonal kpis in web applications. In: Proceedings of the 2018 World Wide Web Conference, International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE, WWW ’18, pp. 187–196 (2018). https://doi.org/10.1145/3178876.3185996

  • Xu, X., Liu, H., Yao, M.: Recent progress of anomaly detection. Complexity 2019, 5 (2019)

    Article  Google Scholar 

  • Yang, P., Kong, L., Chen, G .: Spectrum sharing for 5g/6g urllc: research frontiers and standards. In: IEEE communications standards magazine, pp. 1–12 (2021). https://doi.org/10.1109/MCOMSTD.001.2000054

  • Yu, G., Cai, Z., Wang, S., Chen, H., Liu, F., Liu, A.: Unsupervised online anomaly detection with parameter adaptation for kpi abrupt changes. IEEE Trans. Netw. Serv. Manage. 17(3), 1294–1308 (2020). https://doi.org/10.1109/TNSM.2019.2962701

    Article  Google Scholar 

  • Zhang, L., Xiao, M., Wu, G., Alam, M., Liang, Y.C., Li, S.: A survey of advanced techniques for spectrum sharing in 5g networks. IEEE Wirel. Commun. 24(5), 44–51 (2017). https://doi.org/10.1109/MWC.2017.1700069

    Article  Google Scholar 

  • Zhang, Y., Zhang, Q., Wu, S.: Entropy-based robust spectrum sensing in cognitive radio. Commun. IET 4, 428–436 (2010a). https://doi.org/10.1049/iet-com.2009.0389

    Article  Google Scholar 

  • Zhang, Y.L., Zhang, Q.Y., Melodia, T.: A frequency-domain entropy-based detector for robust spectrum sensing in cognitive radio networks. IEEE Commun. Lett. 14(6), 533–535 (2010b). https://doi.org/10.1109/LCOMM.2010.06.091954

    Article  Google Scholar 

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Acknowledgments

This work is supported by National Key Research and Development Plan, China (Grant No. 2020YFB1710900).

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Authors and Affiliations

  1. Center for Energy-Efficient Computing and Applications, Peking University, Beijing, China

    Haotian Xu, Xiangtian Ma, Chengke Wang, Xiong Wang & Chenren Xu

  2. Beijing Yunzhiruantong Info Tech Ltd., Beijing, China

    Feng Gao

  3. Shanghai Jiao Tong University, Shanghai, China

    Linghe Kong

Authors
  1. Haotian Xu
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  2. Xiangtian Ma
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  3. Chengke Wang
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  4. Xiong Wang
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  5. Chenren Xu
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  6. Feng Gao
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  7. Linghe Kong
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Correspondence to Haotian Xu.

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Xu, H., Ma, X., Wang, C. et al. A neural network approach for wireless spectrum anomaly detection in 5G-unlicensed network. CCF Trans. Pervasive Comp. Interact. 4, 465–473 (2022). https://doi.org/10.1007/s42486-021-00075-1

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