August & September  2019, 12(4&5): 1371-1384. doi: 10.3934/dcdss.2019094

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

Modern Educational Technology Center, Pingdingshan University, Pingdingshan, China

* Corresponding author: Li Gang

Received  July 2017 Revised  November 2017 Published  November 2018

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.

Citation: Li Gang. An optimization detection algorithm for complex intrusion interference signal in mobile wireless network. Discrete & Continuous Dynamical Systems - S, 2019, 12 (4&5) : 1371-1384. doi: 10.3934/dcdss.2019094
References:
[1]

M. A. Ambusaidi, X. He, P. Nanda and et al, Building an intrusion detection system using a filter-based feature selection algorithm, IEEE Trans. Comput., 65 (2016), 2986-2998. doi: 10.1109/TC.2016.2519914.  Google Scholar

[2]

M. Andreolini, M. Colajanni and M. Marchetti, A Collaborative Framework for Intrusion Detection in Mobile Networks, Information Sciences, 2015. Google Scholar

[3]

M. Arthur and K. Kannan, Cross-layer based multiclass intrusion detection system for secure multicast communication of manet in military networks, Wireless Networks, 1035-1059. Google Scholar

[4]

G. BrinkmannM. Preissmann and D. Sasaki, Snarks with total chromatic number 5. maloney, g.r. 2015. on substitution tilings of the plane with n-fold rotational symmetry, Discrete Mathematics and Theoretical Computer Science, 17 (2015), 369-382.   Google Scholar

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K. A. P. CostaL. A. M. PereiraR. Y. M. Nakamura and C. R. Pereira, A nature-inspired approach to speed up optimum-path forest clustering and its application to intrusion detection in computer networks, Information Sciences, 294 (2015), 95-108.  doi: 10.1016/j.ins.2014.09.025.  Google Scholar

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Y. H. Q., Network intrusion small signal detection model based on optimization particle swarm algorithm, Bulletin of Science and Technology, 193-195. Google Scholar

[7]

L. I. Hui-Fang and X. G. Peng, Mobile intelligent terminal intrusion detection method based on cloud computing, Computer Simulation, 380-384. Google Scholar

[8]

A. Khalili and A. Sami, Sysdetect: A systematic approach to critical state determination for industrial intrusion detection systems using apriori algorithm, Journal of Process Control, 32 (2015), 154-160.   Google Scholar

[9]

D. Kim, The development of 'water strider' inquiry learning program for improving scientific inquiry learning ability in the chapter 'the little creatures world' of the korea elementary school 5th grade science textbook, Eurasia Journal of Mathematics Science and Technology Education, 13 (2007), 3325-3348.   Google Scholar

[10]

Z. Lin and J. R. Wang, New riemann-liouville fractional hermite-hadamard inequalities via two kinds of convex functions, Journal of Interdisciplinary Mathematics, 20 (2017), 357-382.   Google Scholar

[11]

S. Masarat, S. Sharifian and H. Taheri, Modified parallel random forest for intrusion detection systems, Journal of Supercomputing, 2235-2258. Google Scholar

[12]

A. MilenkoskiM. VieiraS. KounevA. Avritzer and B. D. Payne, Evaluating computer intrusion detection systems: A survey of common practices, Acm Computing Surveys, 48 (2015), 1-41.   Google Scholar

[13]

D. MoonS. B. Pan and I. Kim, Host-based intrusion detection system for secure human-centric computing, Journal of Supercomputing, 72 (2016), 2520-2536.   Google Scholar

[14]

M. N.D. R. and D. S. K., A novel approach for efficient usage of intrusion detection system in mobile ad hoc networks, IEEE Transactions on Vehicular Technology, 120 (2017), 1684-1695.   Google Scholar

[15]

P. NaderP. Honeine and P. Beauseroy, Online one-class classification for intrusion detection based on the mahalanobis distance, Journal of Materials Science, 23 (2015), 1260-1264.   Google Scholar

[16]

M. M. RathoreA. Ahmad and A. Paul, Real time intrusion detection system for ultra-high-speed big data environments, Journal of Supercomputing, 72 (2016), 3489-3510.   Google Scholar

[17]

D. Samfat and R. Molva, Idamn: An intrusion detection architecture for mobile networks, IEEE Journal on Selected Areas in Communications, 15 (1997), 1373-1380.   Google Scholar

[18]

E. VasilomanolakisS. Karuppayah and M. Fischer, Taxonomy and survey of collaborative intrusion detection, Acm Computing Surveys, 47 (2015), 1-33.   Google Scholar

[19]

E. ViegasA. O. SantinA. FrancaR. JasinskiV. A. Pedroni and L. S. Oliveira, Towards an energy-efficient anomaly-based intrusion detection engine for embedded systems, IEEE Transactions on Computers, 66 (2017), 163-177.  doi: 10.1109/TC.2016.2560839.  Google Scholar

[20]

B. W.J. A. and P. M., Improving network intrusion detection system performance through quality of service configuration and parallel technology, Journal of Computer and System Sciences, 48 (2015), 981-999.   Google Scholar

[21]

P. W., H. Q., M. X. J. and et al, A dynamic network threat assessment method based on multi-source information fusion, Journal of China Academy of Electronics and Information Technology, 17 (2016), 250-256. Google Scholar

[22]

R. W. W., H. L. and Z. K., Intrusion alert correlation model based on data mining and ontology, Journal of Jilin University (Engineering and Technology Edition), 899-906. Google Scholar

[23]

X. Zhang and S. L. Yi, Green building evaluation methodology under ecological view, Journal of Discrete Mathematical Sciences & Cryptography, 20 (2017), 79-90.   Google Scholar

[24]

Q. ZhuC. FungR. Boutaba and T. Basar, Guidex: A game-theoretic incentive-based mechanism for intrusion detection networks, IEEE Journal on Selected Areas in Communications, 30 (2012), 2220-2230.   Google Scholar

show all references

References:
[1]

M. A. Ambusaidi, X. He, P. Nanda and et al, Building an intrusion detection system using a filter-based feature selection algorithm, IEEE Trans. Comput., 65 (2016), 2986-2998. doi: 10.1109/TC.2016.2519914.  Google Scholar

[2]

M. Andreolini, M. Colajanni and M. Marchetti, A Collaborative Framework for Intrusion Detection in Mobile Networks, Information Sciences, 2015. Google Scholar

[3]

M. Arthur and K. Kannan, Cross-layer based multiclass intrusion detection system for secure multicast communication of manet in military networks, Wireless Networks, 1035-1059. Google Scholar

[4]

G. BrinkmannM. Preissmann and D. Sasaki, Snarks with total chromatic number 5. maloney, g.r. 2015. on substitution tilings of the plane with n-fold rotational symmetry, Discrete Mathematics and Theoretical Computer Science, 17 (2015), 369-382.   Google Scholar

[5]

K. A. P. CostaL. A. M. PereiraR. Y. M. Nakamura and C. R. Pereira, A nature-inspired approach to speed up optimum-path forest clustering and its application to intrusion detection in computer networks, Information Sciences, 294 (2015), 95-108.  doi: 10.1016/j.ins.2014.09.025.  Google Scholar

[6]

Y. H. Q., Network intrusion small signal detection model based on optimization particle swarm algorithm, Bulletin of Science and Technology, 193-195. Google Scholar

[7]

L. I. Hui-Fang and X. G. Peng, Mobile intelligent terminal intrusion detection method based on cloud computing, Computer Simulation, 380-384. Google Scholar

[8]

A. Khalili and A. Sami, Sysdetect: A systematic approach to critical state determination for industrial intrusion detection systems using apriori algorithm, Journal of Process Control, 32 (2015), 154-160.   Google Scholar

[9]

D. Kim, The development of 'water strider' inquiry learning program for improving scientific inquiry learning ability in the chapter 'the little creatures world' of the korea elementary school 5th grade science textbook, Eurasia Journal of Mathematics Science and Technology Education, 13 (2007), 3325-3348.   Google Scholar

[10]

Z. Lin and J. R. Wang, New riemann-liouville fractional hermite-hadamard inequalities via two kinds of convex functions, Journal of Interdisciplinary Mathematics, 20 (2017), 357-382.   Google Scholar

[11]

S. Masarat, S. Sharifian and H. Taheri, Modified parallel random forest for intrusion detection systems, Journal of Supercomputing, 2235-2258. Google Scholar

[12]

A. MilenkoskiM. VieiraS. KounevA. Avritzer and B. D. Payne, Evaluating computer intrusion detection systems: A survey of common practices, Acm Computing Surveys, 48 (2015), 1-41.   Google Scholar

[13]

D. MoonS. B. Pan and I. Kim, Host-based intrusion detection system for secure human-centric computing, Journal of Supercomputing, 72 (2016), 2520-2536.   Google Scholar

[14]

M. N.D. R. and D. S. K., A novel approach for efficient usage of intrusion detection system in mobile ad hoc networks, IEEE Transactions on Vehicular Technology, 120 (2017), 1684-1695.   Google Scholar

[15]

P. NaderP. Honeine and P. Beauseroy, Online one-class classification for intrusion detection based on the mahalanobis distance, Journal of Materials Science, 23 (2015), 1260-1264.   Google Scholar

[16]

M. M. RathoreA. Ahmad and A. Paul, Real time intrusion detection system for ultra-high-speed big data environments, Journal of Supercomputing, 72 (2016), 3489-3510.   Google Scholar

[17]

D. Samfat and R. Molva, Idamn: An intrusion detection architecture for mobile networks, IEEE Journal on Selected Areas in Communications, 15 (1997), 1373-1380.   Google Scholar

[18]

E. VasilomanolakisS. Karuppayah and M. Fischer, Taxonomy and survey of collaborative intrusion detection, Acm Computing Surveys, 47 (2015), 1-33.   Google Scholar

[19]

E. ViegasA. O. SantinA. FrancaR. JasinskiV. A. Pedroni and L. S. Oliveira, Towards an energy-efficient anomaly-based intrusion detection engine for embedded systems, IEEE Transactions on Computers, 66 (2017), 163-177.  doi: 10.1109/TC.2016.2560839.  Google Scholar

[20]

B. W.J. A. and P. M., Improving network intrusion detection system performance through quality of service configuration and parallel technology, Journal of Computer and System Sciences, 48 (2015), 981-999.   Google Scholar

[21]

P. W., H. Q., M. X. J. and et al, A dynamic network threat assessment method based on multi-source information fusion, Journal of China Academy of Electronics and Information Technology, 17 (2016), 250-256. Google Scholar

[22]

R. W. W., H. L. and Z. K., Intrusion alert correlation model based on data mining and ontology, Journal of Jilin University (Engineering and Technology Edition), 899-906. Google Scholar

[23]

X. Zhang and S. L. Yi, Green building evaluation methodology under ecological view, Journal of Discrete Mathematical Sciences & Cryptography, 20 (2017), 79-90.   Google Scholar

[24]

Q. ZhuC. FungR. Boutaba and T. Basar, Guidex: A game-theoretic incentive-based mechanism for intrusion detection networks, IEEE Journal on Selected Areas in Communications, 30 (2012), 2220-2230.   Google Scholar

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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