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An optimization detection algorithm for complex intrusion interference signal in mobile wireless network

  • * Corresponding author: Li Gang

    * Corresponding author: Li Gang
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  • At present, when detecting intrusive interference signals in classified form, the effect of channel denoising is very poor, and the characteristics of the extracted signals are not clear, which can not achieve effective detection of intrusion signals. An algorithm based on wavelet packet frequency hopping estimation for complex network intrusion detection is proposed in this paper. The soft and hard threshold method is used for wavelet coefficient decomposition, threshold processing, and signal reconstruction; according to probability statistics, a new sequence is composed of the spectral amplitude corresponding to the same frequency of each random variable in a random process and the spectrum matrix of intrusion interference signal is formed, so as to extract the characteristic spectrum of intrusion interference signal; by using the energy balance method, Gauss stochastic wavelet characteristics of intrusion signal can be simulated. The results of network intrusion detection are obtained by the Gauss additivity of the high-order cumulants of the network intrusion. The three edge centroid positioning method is applied to achieve the high-precision location of the intrusion point. Experiments show that the algorithm effectively improves the network channel denoising and the feature extraction effect of the intrusion signal, and it is also better than the current algorithm for the detection and location of the interference signals.

    Mathematics Subject Classification: 51A40.


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  • Figure 1.  hard and soft threshold functions

    Figure 2.  Improved threshold function

    Figure 3.  Selection rules of wavelet function and threshold

    Figure 4.  principle of TDOA distance measurement

    Figure 5.  Schematic diagram of three point location algorithm for network intrusion interference signal

    Figure 6.  Experimental model

    Figure 7.  Comparison of the effect of different algorithms on the denoising of wireless network

    Figure 8.  Comparison of the effect of different algorithms on the feature extraction of wireless network intrusion signal

    Figure 10.  Comparison of the effect of different algorithms on the location of intrusion signal

    Figure 9.  Comparison of the effect of different algorithms on the network intrusion detection

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