Secure Communication via Chaotic Synchronization Based on Reservoir Computing

Information security occupies a very important part of national security. Chaos communication can provide high-level physical layer security, but its harsh claims on the chaotic system parameters of the transmitter and the receiver resulting in reduced synchronization coefficient and more difficult...

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Vydáno v:IEEE transaction on neural networks and learning systems Ročník 35; číslo 1; s. 285 - 299
Hlavní autoři: Liu, Jiayue, Zhang, Jianguo, Wang, Yuncai
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States IEEE 01.01.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
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ISSN:2162-237X, 2162-2388, 2162-2388
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Abstract Information security occupies a very important part of national security. Chaos communication can provide high-level physical layer security, but its harsh claims on the chaotic system parameters of the transmitter and the receiver resulting in reduced synchronization coefficient and more difficult consistent synchronization of point to multipoint networking. In this article, a chaotic synchronization and communication system based on reservoir computing (RC) has been proposed. In this scheme, the trained RC highly synchronized with the emitter acts as the receiver with simplified structure under the premise of ensuring safety. Simultaneously, the cross-prediction algorithm has been proposed to weaken the accumulation effect of prediction synchronization error of RC and facilitate the realization of long-term communication. Furthermore, the tolerance of the system performance to the signal-to-noise ratio with the variations of the mask coefficients has been investigated, and the optimal operation point under the condition of the adjustable number of nodes and leakage rate of RC has been numerically analyzed. The simulation results show that the normalized mean-square error of synchronization of 10−6 magnitude and the bit error rate of decryption at 10−8 level can be obtained. Finally, from the operational perspective, a 100-m short-distance experiment confirms that its communication performance is consistent with the simulation results. We strongly believe that the proposed system offers the opportunity of a new research direction in chaotic secure communications.
AbstractList Information security occupies a very important part of national security. Chaos communication can provide high-level physical layer security, but its harsh claims on the chaotic system parameters of the transmitter and the receiver resulting in reduced synchronization coefficient and more difficult consistent synchronization of point to multipoint networking. In this article, a chaotic synchronization and communication system based on reservoir computing (RC) has been proposed. In this scheme, the trained RC highly synchronized with the emitter acts as the receiver with simplified structure under the premise of ensuring safety. Simultaneously, the cross-prediction algorithm has been proposed to weaken the accumulation effect of prediction synchronization error of RC and facilitate the realization of long-term communication. Furthermore, the tolerance of the system performance to the signal-to-noise ratio with the variations of the mask coefficients has been investigated, and the optimal operation point under the condition of the adjustable number of nodes and leakage rate of RC has been numerically analyzed. The simulation results show that the normalized mean-square error of synchronization of 10-6 magnitude and the bit error rate of decryption at 10-8 level can be obtained. Finally, from the operational perspective, a 100-m short-distance experiment confirms that its communication performance is consistent with the simulation results. We strongly believe that the proposed system offers the opportunity of a new research direction in chaotic secure communications.
Information security occupies a very important part of national security. Chaos communication can provide high-level physical layer security, but its harsh claims on the chaotic system parameters of the transmitter and the receiver resulting in reduced synchronization coefficient and more difficult consistent synchronization of point to multipoint networking. In this article, a chaotic synchronization and communication system based on reservoir computing (RC) has been proposed. In this scheme, the trained RC highly synchronized with the emitter acts as the receiver with simplified structure under the premise of ensuring safety. Simultaneously, the cross-prediction algorithm has been proposed to weaken the accumulation effect of prediction synchronization error of RC and facilitate the realization of long-term communication. Furthermore, the tolerance of the system performance to the signal-to-noise ratio with the variations of the mask coefficients has been investigated, and the optimal operation point under the condition of the adjustable number of nodes and leakage rate of RC has been numerically analyzed. The simulation results show that the normalized mean-square error of synchronization of 10−6 magnitude and the bit error rate of decryption at 10−8 level can be obtained. Finally, from the operational perspective, a 100-m short-distance experiment confirms that its communication performance is consistent with the simulation results. We strongly believe that the proposed system offers the opportunity of a new research direction in chaotic secure communications.
Information security occupies a very important part of national security. Chaos communication can provide high-level physical layer security, but its harsh claims on the chaotic system parameters of the transmitter and the receiver resulting in reduced synchronization coefficient and more difficult consistent synchronization of point to multipoint networking. In this article, a chaotic synchronization and communication system based on reservoir computing (RC) has been proposed. In this scheme, the trained RC highly synchronized with the emitter acts as the receiver with simplified structure under the premise of ensuring safety. Simultaneously, the cross-prediction algorithm has been proposed to weaken the accumulation effect of prediction synchronization error of RC and facilitate the realization of long-term communication. Furthermore, the tolerance of the system performance to the signal-to-noise ratio with the variations of the mask coefficients has been investigated, and the optimal operation point under the condition of the adjustable number of nodes and leakage rate of RC has been numerically analyzed. The simulation results show that the normalized mean-square error of synchronization of 10-6 magnitude and the bit error rate of decryption at 10-8 level can be obtained. Finally, from the operational perspective, a 100-m short-distance experiment confirms that its communication performance is consistent with the simulation results. We strongly believe that the proposed system offers the opportunity of a new research direction in chaotic secure communications.Information security occupies a very important part of national security. Chaos communication can provide high-level physical layer security, but its harsh claims on the chaotic system parameters of the transmitter and the receiver resulting in reduced synchronization coefficient and more difficult consistent synchronization of point to multipoint networking. In this article, a chaotic synchronization and communication system based on reservoir computing (RC) has been proposed. In this scheme, the trained RC highly synchronized with the emitter acts as the receiver with simplified structure under the premise of ensuring safety. Simultaneously, the cross-prediction algorithm has been proposed to weaken the accumulation effect of prediction synchronization error of RC and facilitate the realization of long-term communication. Furthermore, the tolerance of the system performance to the signal-to-noise ratio with the variations of the mask coefficients has been investigated, and the optimal operation point under the condition of the adjustable number of nodes and leakage rate of RC has been numerically analyzed. The simulation results show that the normalized mean-square error of synchronization of 10-6 magnitude and the bit error rate of decryption at 10-8 level can be obtained. Finally, from the operational perspective, a 100-m short-distance experiment confirms that its communication performance is consistent with the simulation results. We strongly believe that the proposed system offers the opportunity of a new research direction in chaotic secure communications.
Author Zhang, Jianguo
Wang, Yuncai
Liu, Jiayue
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Cites_doi 10.1063/1.5081797
10.35848/1347-4065/ab7860
10.1142/s021812749300129x
10.1364/prj.409114
10.1103/physrevapplied.12.024052
10.1063/5.0017974
10.1364/osac.2.003422
10.1063/1.5118725
10.1103/physreve.98.012215
10.1007/s11770-018-0681-x
10.1109/82.246163
10.1364/oe.26.032404
10.1063/1.5120867
10.1109/SPAWC.2018.8445920
10.1109/JSTQE.2019.2936947
10.1016/j.chaos.2020.110131
10.1038/ncomms1476
10.1109/JLT.2010.2050858
10.1007/978-3-642-31965-5_5
10.1364/oe.24.008679
10.1007/978-3-319-91253-0_22
10.1016/j.cosrev.2009.03.005
10.3390/s130202494
10.1103/physreve.78.036203
10.1364/ol.29.002497
10.1364/ol.44.005776
10.1109/JSTQE.2020.3011879
10.1038/nature04275
10.1109/MCOM.2019.1900093
10.1016/j.physa.2019.122273
10.1103/physrevlett.120.024102
10.1109/JPHOT.2019.2931615
10.1016/j.jmmm.2020.167251
10.1103/physrevlett.97.123902
10.1109/ACCESS.2019.2905422
10.1007/bf00114800
10.1063/1.5120822
10.1063/1.5022276
10.1109/TNNLS.2016.2598655
10.1103/physreve.99.042203
10.1063/1.5120710
10.1063/1.4979665
10.1103/physreva.72.053810
10.1142/s0218127409023688
10.1016/j.optcom.2018.10.014
10.1162/neco_a_01198
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References ref13
ref35
ref12
ref34
ref15
ref37
ref14
ref36
ref31
ref30
ref11
ref33
ref10
ref32
ref2
ref1
ref17
ref39
ref16
ref38
ref19
ref18
ref24
ref46
ref23
ref45
ref26
ref25
ref20
ref42
ref41
ref22
ref44
ref21
ref43
ref28
ref27
ref29
ref8
ref7
ref9
ref4
ref3
ref6
ref5
ref40
References_xml – ident: ref19
  doi: 10.1063/1.5081797
– ident: ref15
  doi: 10.35848/1347-4065/ab7860
– ident: ref46
  doi: 10.1142/s021812749300129x
– ident: ref35
  doi: 10.1364/prj.409114
– ident: ref20
  doi: 10.1103/physrevapplied.12.024052
– ident: ref24
  doi: 10.1063/5.0017974
– ident: ref1
  doi: 10.1364/osac.2.003422
– ident: ref22
  doi: 10.1063/1.5118725
– ident: ref36
  doi: 10.1103/physreve.98.012215
– ident: ref37
  doi: 10.1007/s11770-018-0681-x
– ident: ref4
  doi: 10.1109/82.246163
– ident: ref3
  doi: 10.1364/oe.26.032404
– ident: ref31
  doi: 10.1063/1.5120867
– ident: ref11
  doi: 10.1109/SPAWC.2018.8445920
– ident: ref33
  doi: 10.1109/JSTQE.2019.2936947
– ident: ref14
  doi: 10.1016/j.chaos.2020.110131
– ident: ref34
  doi: 10.1038/ncomms1476
– ident: ref10
  doi: 10.1109/JLT.2010.2050858
– ident: ref41
  doi: 10.1007/978-3-642-31965-5_5
– ident: ref28
  doi: 10.1364/oe.24.008679
– ident: ref21
  doi: 10.1007/978-3-319-91253-0_22
– ident: ref38
  doi: 10.1016/j.cosrev.2009.03.005
– ident: ref6
  doi: 10.3390/s130202494
– ident: ref43
  doi: 10.1103/physreve.78.036203
– ident: ref45
  doi: 10.1364/ol.29.002497
– ident: ref2
  doi: 10.1364/ol.44.005776
– ident: ref18
  doi: 10.1109/JSTQE.2020.3011879
– ident: ref7
  doi: 10.1038/nature04275
– ident: ref12
  doi: 10.1109/MCOM.2019.1900093
– ident: ref29
  doi: 10.1016/j.physa.2019.122273
– ident: ref39
  doi: 10.1103/physrevlett.120.024102
– ident: ref8
  doi: 10.1109/JPHOT.2019.2931615
– ident: ref13
  doi: 10.1016/j.jmmm.2020.167251
– ident: ref9
  doi: 10.1103/physrevlett.97.123902
– ident: ref17
  doi: 10.1109/ACCESS.2019.2905422
– ident: ref5
  doi: 10.1007/bf00114800
– ident: ref25
  doi: 10.1063/1.5120822
– ident: ref30
  doi: 10.1063/1.5022276
– ident: ref32
  doi: 10.1109/TNNLS.2016.2598655
– ident: ref26
  doi: 10.1103/physreve.99.042203
– ident: ref27
  doi: 10.1063/1.5120710
– ident: ref40
  doi: 10.1063/1.4979665
– ident: ref44
  doi: 10.1103/physreva.72.053810
– ident: ref42
  doi: 10.1142/s0218127409023688
– ident: ref16
  doi: 10.1016/j.optcom.2018.10.014
– ident: ref23
  doi: 10.1162/neco_a_01198
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Snippet Information security occupies a very important part of national security. Chaos communication can provide high-level physical layer security, but its harsh...
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SubjectTerms Algorithms
Bit error rate
Chaos synchronization and communication
Chaos theory
Chaotic communication
Communication
Communications systems
Computation
cross-prediction algorithm
Cryptography
Emitters
Neural networks
Prediction algorithms
Reservoir computing
reservoir computing (RC)
Signal to noise ratio
Synchronism
Synchronization
Title Secure Communication via Chaotic Synchronization Based on Reservoir Computing
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