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Cluster computing data mining based on massive intrusion interference constraints in hybrid networks

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Abstract

To solve the problem that massive intrusion data in hybrid networks greatly interfere network intrusion detection and cause relatively great difficulty to detection due to their frequency discontinuity, a mining algorithm of massive intrusion cluster computing data in hybrid networks based on spectral feature extraction under fixed constraints of time–frequency window is proposed in this paper. The multi-component cross-detection method is used to collect massive intrusion information in hybrid networks and construct a model of massive intrusion signal in hybrid networks. Cascading notch method is used to suppress intrusion interference under constraints of fixed time–frequency window, and extract fundamental quantity and primary function with locality in massive interference information, and obtain a complete energy distribution spectrum on the time–frequency plane. The energy distribution spectrum is used as guidance function to realize cluster calculating data mining with massive intrusion interference constraints. Simulation results show that, in the intrusion detection process of signal-to-noise ratio from − 15 to − 5 dB, the detection accuracy of the method proposed in this paper is always better than that of others. When the signal-to-noise ratio is − 9 dB, the detection accuracy of this method is over 90%. When the signal-to-noise ratio is −9 dB, the detection accuracy of this method can reach 100%. About the detection time, especially after the Number of intrusion date is 2000, the detection time difference between the three methods is increased, and the detection time of other methods is 2–3 times of paper method. The method proposed can accurately locate mass intrusion distribution sources in hybrid networks under the large frequency oscillation of intrusion data in the hybrid networks, realize network intrusion detection and interference suppression and can filter interference information well, so this method improves intrusion detection probability in hybrid networks.

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References

  1. Hsieh, J.W., Lam, K.Y., Huang, P.C., et al.: Block-based multi-version B+-Tree for flash-based embedded database systems [J]. IEEE Trans. Comput. 64(4), 925–940 (2015)

    Article  MathSciNet  Google Scholar 

  2. Dai, L., Gao, X., Wang, Z.: Energy-efficient hybrid precoding based on successive interference cancelation for millimeter-wave massive MIMO systems [C]. In: Radio and Antenna Days of the Indian Ocean, pp. 1–2. IEEE (2015)

  3. Zhang, W.M., Chen, Q.Z.: Network intrusion detection algorithm based on HHT with shift hierarchical control [J]. Comput. Sci. 41(12), 107–111 (2014)

    MathSciNet  Google Scholar 

  4. Jia, W., Xiao, L., Zhu, D.: A mini-ripple control method for doubly salient electromagnetic motor control system [C]. In: Applied Power Electronics Conference and Exposition, pp. 1886–1890. IEEE (2017)

  5. Chen, Y.C., Lin, Z.H., Zhao, X., et al.: Deep learning-based classification of hyperspectral data [J]. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 7(6), 2094–2107 (2014)

    Article  Google Scholar 

  6. Wan, L.J., Tang, K., Li, M.Z., et al.: Collaborative active and semisupervised learning for hyperspectral remote sensing image classification [J]. IEEE Trans. Geosci. Remote Sens. 53(5), 2384–2396 (2015)

    Article  Google Scholar 

  7. Hao, H.: Multi component LFM signal detection and parameter estimation based on EEMD-FRFT [J]. Opt. Int. J. Light Electron Opt. 124(23), 6093–6096 (2013)

    Article  Google Scholar 

  8. Meng, B., Zhang, J.L., Lu, J.T.: Automatic analysis of authentication of OpenID connect protocol based on the computational model [J]. J. South Central Univ. Natl. 35(3), 123–129 (2016)

    Google Scholar 

  9. Wang, H.X., Wang, S.Y., Wang, X., et al.: Analysis of LFM signals and improvement of IFM system [J]. Acta Armamentarii 35(8), 1193–1199 (2014)

    Google Scholar 

  10. Yan, K.Q., Wang, S.C., Liu, C.W.: A hybrid intrusion detection system of cluster-based wireless sensor networks [J]. Lect. Notes Eng. Comput. Sci. 2174(1), 477–480 (2009)

    Google Scholar 

  11. Thulasiraman, P., Shen, X.: Interference aware resource allocation for hybrid hierarchical wireless networks [J]. Comput. Netw. 54(13), 2271–2280 (2010)

    Article  Google Scholar 

  12. Tran, H., Zepernick, H.J., Phan, H., et al.: Cognitive cooperative networks with cluster-based relaying under interference constraints[C]. In: International Conference on Ubiquitous & Future Networks, pp. 542–546. IEEE (2013)

  13. Xie, M.G., Jia, X.D., Zhou, M.: Research on energy efficiency of hybrid networks based on massive MIMO and D2D [J]. J. Signal Process. 16(24), 53–58 (2017)

    Google Scholar 

  14. Feng, W., Feng, S., Ding, Y., et al.: Scalable cross-layer multipath routing under interference constraints in wireless mesh networks [J]. Int. J. Ad Hoc Ubiquitous Comput. 21(3), 194–206 (2016)

    Article  Google Scholar 

  15. Liu, Y., Li, Y., Man, H., et al.: A hybrid data mining anomaly detection technique in ad hoc networks [J]. Int. J. Wirel. Mobile Comput. 2(1), 37–46 (2007)

    Article  Google Scholar 

  16. Yousef, E.L., Toumanari, A., Bouirden, A., et al.: Intrusion detection techniques in wireless sensor network using data mining algorithms: comparative evaluation based on attacks detection [J]. Int. J. Adv. Comput. Sci. Appl. 6(9), 13–17 (2015)

    Google Scholar 

  17. Ding, Y., Pongaliur, K., Xiao, L.: Channel allocation and routing in hybrid multichannel multiradio wireless mesh networks [J]. IEEE Trans. Mobile Comput. 12(2), 206–218 (2013)

    Article  Google Scholar 

  18. Shen, H., Li, Z., Yu, L.: A P2P-based market-guided distributed routing mechanism for high-throughput hybrid wireless networks [J]. IEEE Trans. Mobile Comput. 14(2), 245–260 (2015)

    Article  Google Scholar 

  19. Kurras, M., Thiele, L., Caire, G.: Multi-stage beamforming for interference coordination in massive MIMO networks[C]. In: Asilomar Conference on Signals, Systems and Computers, pp. 700–703. IEEE (2015)

  20. An, K., Lin, M., Zhu, W.P., et al.: Outage performance of cognitive hybrid satellite-terrestrial networks with interference constraint [J]. IEEE Trans. Veh. Technol. 65(11), 9397–9404 (2016)

    Article  Google Scholar 

  21. Wu, H.H., Mu, Y., Qu, Z.F., et al.: Similarity and nearness relational degree based on panel data [J]. Control Decis. 31(03), 555–558 (2016)

    MATH  Google Scholar 

Download references

Funding

Funding were provided by National Natural Science Foundation of China (Grant No. 61461017), the Hainan Natural Science Foundation Innovation Research Team Project (Grant No. 2017CXTD0004), the Hainan Province Key Research and Development Projects (Grant No. ZDYF2016002), the Innovative Research Project of Postgraduates in Hainan Province (Grant No. Hyb2017-07), the Open Topic of State Key Laboratory of Marine Resources Utilization in South China Sea of Hainan University (Grant No. 2016013A), and the Key Laboratory of Sanya Project (Grant No. L1410).

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Authors and Affiliations

  1. State Key Laboratory of Marine Resources Utilization in South China Sea, Hainan University, Haikou, 570228, Hainan, China

    Kun Zhang, Chong Shen, Haifeng Wang & Qian Gao

  2. College of Ocean Information Engineering, Hainan Tropical Ocean University, Sanya, 572022, Hainan, China

    Kun Zhang, Haifeng Wang, Zhuang Li & Xiaoyan Chen

  3. College of Information Science and Technology, Hainan University, Haikou, 570228, Hainan, China

    Kun Zhang, Chong Shen & Qian Gao

Authors
  1. Kun Zhang
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  2. Chong Shen
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  3. Haifeng Wang
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  4. Zhuang Li
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  5. Qian Gao
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  6. Xiaoyan Chen
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Correspondence to Chong Shen.

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Zhang, K., Shen, C., Wang, H. et al. Cluster computing data mining based on massive intrusion interference constraints in hybrid networks. Cluster Comput 22 (Suppl 3), 7481–7489 (2019). https://doi.org/10.1007/s10586-018-1780-4

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  • DOI: https://doi.org/10.1007/s10586-018-1780-4

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