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

Li Gang

doi:10.3934/dcdss.2019094

Article Contents

2019, Volume 12, Issue 4&5: 1371-1384. Doi: 10.3934/dcdss.2019094 open access

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

Li Gang^,

Modern Educational Technology Center, Pingdingshan University, Pingdingshan, China

* Corresponding author: Li Gang
* Corresponding author: Li Gang

Received: June 30, 2017

Revised: October 31, 2017

Published: March 2019

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Abstract

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.

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Mathematics Subject Classification: 51A40.

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Figure 1. hard and soft threshold functions

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Figure 2. Improved threshold function

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Figure 3. Selection rules of wavelet function and threshold

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Figure 4. principle of TDOA distance measurement

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

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Figure 6. Experimental model

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

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

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

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

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