August & September  2019, 12(4&5): 1327-1340. doi: 10.3934/dcdss.2019091

A high precision data encryption algorithm in wireless network mobile communication

1. 

School of Computer, Pingdingshan University, Pingdingshan, China

2. 

College of Information Engineering, Pingdingshan University, Pingdingshan, China

3. 

Dept. of Mathematics and Statistics, Winona State University, Winona, MN 55987, USA

* Corresponding author: Aiwan Fan

Received  July 2017 Revised  December 2017 Published  November 2018

At present, the MD5 based data encryption algorithm for wireless network mobile communication cannot effectively detect the intrusion data in the mobile communication. Redundant data is not removed, the efficiency of data encryption is low, and the overall communication security is poor. In this paper, a MDEA based data encryption algorithm for wireless network mobile communication is proposed. By applying normalization of communication data to DBN model, using the way of changing one parameter while keeping others, the optimal DBN detection model is built to achieve high-precision detection of intrusion data. Using the signal intensity at different times, the speed and process time of the data level movements are estimated. By estimating the results, the redundant data and inappropriate data are removed, and performed the MDEA operation based on the secret data, introduced random numbers and timestamps to prevent the foreign infiltrations. Experiments show that the algorithm can not only improve the detection quality of intrusion data, but also enhance the cleaning effect of redundant data and in the communication, and enhance data security.

Citation: Aiwan Fan, Qiming Wang, Joyati Debnath. A high precision data encryption algorithm in wireless network mobile communication. Discrete & Continuous Dynamical Systems - S, 2019, 12 (4&5) : 1327-1340. doi: 10.3934/dcdss.2019091
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J. Ahmad and S. O. Hwang, Chaos-based diffusion for highly autocorrelated data in encryption algorithms, Nonlinear Dynamics, 82 (2015), 1839-1850.  doi: 10.1007/s11071-015-2281-0.  Google Scholar

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H. ChenX. Du and Z. Liu, Optical hyperspectral data encryption in spectrum domain by using 3d arnold and gyrator transforms, Spectroscopy Letters, 49 (2016), 103-107.   Google Scholar

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B. CuiZ. Liu and L. Wang, Key-aggregate searchable encryption (kase) for group data sharing via cloud storage, IEEE Transactions on Computers, 65 (2016), 2374-2385.  doi: 10.1109/TC.2015.2389959.  Google Scholar

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M. Gomez-BarreroE. MaioranaJ. GalballyP. Campisi and J. Fierrez, Multi-biometric template protection based on homomorphic encryption, Pattern Recognition, 67 (2017), 149-163.   Google Scholar

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Y. JiangG. LiW. CheY. LiuB. XuG. ShanD. ZhuZ. Su and M. R. Bryce, A neutral dinuclear ir(ⅲ) complex for anti-counterfeiting and data encryption, Chemical Communications, 53 (2017), 3022-3025.   Google Scholar

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M. Khan, A novel image encryption scheme based on multiple chaotic s-boxes, Nonlinear Dynamics, 82 (2015), 527-533.  doi: 10.1007/s11071-015-2173-3.  Google Scholar

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A. KhedrG. Gulak and V. Vaikuntanathan, Shield: Scalable homomorphic implementation of encrypted data-classifiers, IEEE Transactions on Computers, 65 (2016), 2848-2858.  doi: 10.1109/TC.2015.2500576.  Google Scholar

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K. Lata, Secure data aggregation in wireless sensor networks using homomorphic encryption, International Journal of Electronics, 102 (2015), 690-702.   Google Scholar

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M. A. Lei, H. X. Yang and J. P. Liu, User privacy data storage method under big data environment, Computer Simulation, 465-468. Google Scholar

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H. LiD. LiuY. Dai and T. H. Luan, Engineering searchable encryption of mobile cloud networks: When qoe meets qop, IEEE Wireless Communications, 22 (2015), 74-80.   Google Scholar

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M. LiD. XiaoA. Kulsoom and Y. Zhang, Improved reversible data hiding for encrypted images using full embedding strategy, Electronics Letters, 51 (2015), 690-691.   Google Scholar

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H. Y. Lin, Location-based data encryption for wireless sensor network using dynamic keys, Wireless Networks, 21 (2015), 1-8.   Google Scholar

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S. T. OpitzK. NeumannS. Bernholt and U. Harms, How do students understand energy in biology, chemistry, and physics development and validation of an assessment instrument, Journal of Mathematics Science and Technology Education, 13 (2017), 3019-3042.   Google Scholar

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S. T. OpitzK. NeumannS. Bernholt and U. Harms, Students' energy understanding across biology, chemistry, and physics contexts, Journal of Interdisciplinary Mathematics, 20 (2017), 397-415.   Google Scholar

[21]

Z. H. QianD. FengX. Wang and Q. Li, Multipath routing algorithm in m2m network based on load balancing, Journal of Jilin University(Engineering and Technology Edition), 46 (2016), 934-940.   Google Scholar

[22]

S. TrejosJ. F. BarreraA. VelezM. Tebaldi and R. Torroba, Optical approach for the efficient data volume handling in experimentally encrypted data, Journal of Optics, 18 (2016), 065702.   Google Scholar

[23]

J. Xu, Data distributed momdatory secure access method in cloud computing environment, Bulletin of Science & Technology, 189-192. Google Scholar

show all references

References:
[1]

P. A., System and method for combining deduplication and encryption of data, 2015. Google Scholar

[2]

J. Ahmad and S. O. Hwang, Chaos-based diffusion for highly autocorrelated data in encryption algorithms, Nonlinear Dynamics, 82 (2015), 1839-1850.  doi: 10.1007/s11071-015-2281-0.  Google Scholar

[3]

A. ArgyrisE. Pikasis and D. Syvridis, Gb/s one-time-pad data encryption with synchronized chaos-based true random bit generators, Journal of Lightwave Technology, 34 (2016), 5325-5331.   Google Scholar

[4]

J. L. Baril, Avoiding patterns in irreducible permutations, Discrete Mathematics and Theoretical Computer Science, 17 (2006), 13-30.   Google Scholar

[5]

L. BossuetN. DattaC. Mancillas-López and M. Nandi, Elmd: A pipelineable authenticated encryption and its hardware implementation, IEEE Transactions on Computers, 65 (2016), 3318-3331.  doi: 10.1109/TC.2016.2529618.  Google Scholar

[6]

M. D. ChampiriS. SajjadiS. H. Mousavizadegan and F. Moodi, A fuzzy system for evaluation of deteriorated marine steel structures, Journal of Intelligent & Fuzzy Systems, 32 (2017), 1945-1958.   Google Scholar

[7]

H. ChenX. Du and Z. Liu, Optical hyperspectral data encryption in spectrum domain by using 3d arnold and gyrator transforms, Spectroscopy Letters, 49 (2016), 103-107.   Google Scholar

[8]

B. CuiZ. Liu and L. Wang, Key-aggregate searchable encryption (kase) for group data sharing via cloud storage, IEEE Transactions on Computers, 65 (2016), 2374-2385.  doi: 10.1109/TC.2015.2389959.  Google Scholar

[9]

W. D. H., Data authentication and security assurance based on distributed storage system, Journal of China Academy of Electronics and Information Technology, 1 (2015), 613-619. Google Scholar

[10]

M. Gomez-BarreroE. MaioranaJ. GalballyP. Campisi and J. Fierrez, Multi-biometric template protection based on homomorphic encryption, Pattern Recognition, 67 (2017), 149-163.   Google Scholar

[11]

Y. JiangG. LiW. CheY. LiuB. XuG. ShanD. ZhuZ. Su and M. R. Bryce, A neutral dinuclear ir(ⅲ) complex for anti-counterfeiting and data encryption, Chemical Communications, 53 (2017), 3022-3025.   Google Scholar

[12]

M. Khan, A novel image encryption scheme based on multiple chaotic s-boxes, Nonlinear Dynamics, 82 (2015), 527-533.  doi: 10.1007/s11071-015-2173-3.  Google Scholar

[13]

A. KhedrG. Gulak and V. Vaikuntanathan, Shield: Scalable homomorphic implementation of encrypted data-classifiers, IEEE Transactions on Computers, 65 (2016), 2848-2858.  doi: 10.1109/TC.2015.2500576.  Google Scholar

[14]

K. Lata, Secure data aggregation in wireless sensor networks using homomorphic encryption, International Journal of Electronics, 102 (2015), 690-702.   Google Scholar

[15]

M. A. Lei, H. X. Yang and J. P. Liu, User privacy data storage method under big data environment, Computer Simulation, 465-468. Google Scholar

[16]

H. LiD. LiuY. Dai and T. H. Luan, Engineering searchable encryption of mobile cloud networks: When qoe meets qop, IEEE Wireless Communications, 22 (2015), 74-80.   Google Scholar

[17]

M. LiD. XiaoA. Kulsoom and Y. Zhang, Improved reversible data hiding for encrypted images using full embedding strategy, Electronics Letters, 51 (2015), 690-691.   Google Scholar

[18]

H. Y. Lin, Location-based data encryption for wireless sensor network using dynamic keys, Wireless Networks, 21 (2015), 1-8.   Google Scholar

[19]

S. T. OpitzK. NeumannS. Bernholt and U. Harms, How do students understand energy in biology, chemistry, and physics development and validation of an assessment instrument, Journal of Mathematics Science and Technology Education, 13 (2017), 3019-3042.   Google Scholar

[20]

S. T. OpitzK. NeumannS. Bernholt and U. Harms, Students' energy understanding across biology, chemistry, and physics contexts, Journal of Interdisciplinary Mathematics, 20 (2017), 397-415.   Google Scholar

[21]

Z. H. QianD. FengX. Wang and Q. Li, Multipath routing algorithm in m2m network based on load balancing, Journal of Jilin University(Engineering and Technology Edition), 46 (2016), 934-940.   Google Scholar

[22]

S. TrejosJ. F. BarreraA. VelezM. Tebaldi and R. Torroba, Optical approach for the efficient data volume handling in experimentally encrypted data, Journal of Optics, 18 (2016), 065702.   Google Scholar

[23]

J. Xu, Data distributed momdatory secure access method in cloud computing environment, Bulletin of Science & Technology, 189-192. Google Scholar

Figure 1.  multi-layer RBM learning process
Figure 2.  the radiant range of the readers
Figure 3.  data processing structure
Figure 4.  data output structure
Figure 5.  MEDA algorithm structure
Figure 6.  Comparison of the effects of different algorithms on Intrusion Detection
Figure 7.  Comparison of data cleaning effect by different algorithms
Figure 8.  Comparison of security effects between different data encryption algorithms
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