Abstract

Physical layer attacks threaten services transmitted through optical networks. To detect attacks, we present an investigation of optical spectrum feature analysis (OSFA) and recognition. By analyzing the spectral features of optical signals, recognition and detection of unauthorized signals can be realized. In this paper, (1) we theoretically analyzed factors influencing optical spectrum (OS) features and simulated these factors. OSs collected from the simulation are quantitatively analyzed, spectral features are extracted by principal component analysis, and the theoretical derivation is validated. (2) We proposed support vector machine (SVM) and one-dimensional convolutional neural network (1D-CNN) machine-learning OSFA methods. (3) Experimentally collected OSs from commercial small form-factor pluggable modules are used to verify the performance of the SVM and 1D-CNN methods, which achieved 98.54% and 100% recognition accuracies, respectively, demonstrating that the methods are promising solutions for optical network security.

© 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

Full Article  |  PDF Article
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References

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2019 (2)

F. Musumeci, C. Rottondi, A. Nag, I. Macaluso, D. Zibar, M. Ruffini, and M. Tornatore, “An Overview on Application of Machine Learning Techniques in Optical Networks,” IEEE Comm. Surv. and Tutor. 21(2), 1383–1408 (2019).
[Crossref]

L. Yi, T. Liao, L. Huang, L. Xue, P. Li, and W. Hu, “Machine Learning for 100 Gb/s/λ Passive Optical Network,” J. Lightwave Technol. 37(6), 1621–1630 (2019).
[Crossref]

2018 (4)

2017 (7)

J. Zhu and Z. Zhu, “Physical-layer security in MCF-based SDM-EONs: Would crosstalk-aware service provisioning be good enough?” J. Lightwave Technol. 35(22), 4826–4837 (2017).
[Crossref]

S. Yan, A. Aguado, Y. Ou, R. Wang, R. Nejabati, and D. Simeonidou, “Multilayer network analytics with SDN-based monitoring framework,” IEEE/OSA J. Opt. Commun. Netw. 9(2), A271–A279 (2017).
[Crossref]

W. Zhang, C. Zhang, C. Chen, and K. Qiu, “Experimental demonstration of security-enhanced OFDMA-PON using chaotic constellation transformation and pilot-aided secure key agreement,” J. Lightwave Technol. 35(9), 1524–1530 (2017).
[Crossref]

H. Lu, S. Cui, C. Ke, and D. Liu, “Automatic reference optical spectrum retrieval method for ultra-high resolution optical spectrum distortion analysis utilizing integrated machine learning techniques,” Opt. Express 25(26), 32491–32503 (2017).
[Crossref]

Y. Huang, C. L. Gutterman, P. Samadi, P. B. Cho, W. Samoud, C. Ware, M. Lourdiane, G. Zussman, and K. Bergman, “Dynamic mitigation of EDFA power excursions with machine learning,” Opt. Express 25(3), 2245–2258 (2017).
[Crossref] [PubMed]

J. Thrane, J. Wass, M. Piels, J. C. M. Diniz, R. Jones, and D. Zibar, “Machine Learning Techniques for Optical Performance Monitoring From Directly Detected PDM-QAM Signals,” J. Lightwave Technol. 35(4), 868–875 (2017).
[Crossref]

F. N. Khan, K. Zhong, X. Zhou, W. H. Al-Arashi, C. Yu, C. Lu, and A. P. T. Lau, “Joint OSNR monitoring and modulation format identification in digital coherent receivers using deep neural networks,” Opt. Express 25(15), 17767–17776 (2017).
[Crossref] [PubMed]

2016 (4)

D. Gariépy, S. Searcy, G. He, and S. Tibuleac, “Non-intrusive OSNR measurement of polarization-multiplexed signals with spectral shaping and subject to fiber non-linearity with minimum channel spacing of 37.5GHz,” Opt. Express 24(18), 20156–20166 (2016).
[Crossref] [PubMed]

Y. Li, N. Hua, Y. Song, S. Li, and X. Zheng, “Fast lightpath hopping enabled by time synchronization for optical network security,” IEEE Commun. Lett. 20(1), 101–104 (2016).
[Crossref]

N. Skorin-Kapov, M. Furdek, S. Zsigmond, and L. Wosinska, “Physical-layer security in evolving optical networks,” IEEE Commun. Mag. 54(8), 110–117 (2016).
[Crossref]

J. Zhu, B. Zhao, W. Lu, and Z. Zhu, “Attack-aware service provisioning to enhance physical-layer security in multi-domain EONs,” J. Lightwave Technol. 34(11), 2645–2655 (2016).
[Crossref]

2015 (2)

2012 (1)

2011 (1)

2010 (1)

N. Skorin-Kapov, J. Chen, and L. Wosinska, “A new approach to optical networks security: Attack-aware routing and wavelength assignment,” IEEE/ACM Trans. Netw. 18(3), 750–760 (2010).
[Crossref]

2007 (1)

O. Chapelle, “Training a support vector machine in the primal,” Neural Comput. 19(5), 1155–1178 (2007).
[Crossref] [PubMed]

2006 (2)

B. B. Wu and E. E. Narimanov, “A method for secure communications over a public fiber-optical network,” Opt. Express 14(9), 3738–3751 (2006).
[Crossref] [PubMed]

R. Rejeb, M. S. Leeson, and R. J. Green, “Multiple attack localization and identification in all-optical networks,” Opt. Switching Networking 3(1), 41–49 (2006).
[Crossref]

2005 (2)

T. Wu and A. K. Somani, “Cross-talk attack monitoring and localization in all-optical networks,” IEEE/ACM Trans. Netw. 13(6), 1390–1401 (2005).
[Crossref]

C. Mas, I. Tomkos, and O. K. Tonguz, “Failure location algorithm for transparent optical networks,” IEEE J. Sel. Areas Comm. 23(8), 1508–1519 (2005).
[Crossref]

1997 (2)

M. Medard, D. Marquis, R. A. Barry, and S. G. Finn, “Security issues in all-optical networks,” IEEE Netw. 11(3), 42–48 (1997).
[Crossref]

P. N. Belhumeur, J. P. Hespanha, and D. J. Kriegman, “Eigenfaces vs. fisherfaces: Recognition using class specific linear projection,” IEEE Trans. Pattern Anal. Mach. Intell. 19(7), 711–720 (1997).
[Crossref]

Aguado, A.

S. Yan, A. Aguado, Y. Ou, R. Wang, R. Nejabati, and D. Simeonidou, “Multilayer network analytics with SDN-based monitoring framework,” IEEE/OSA J. Opt. Commun. Netw. 9(2), A271–A279 (2017).
[Crossref]

Al-Arashi, W. H.

Aoki, Y.

M. Bouda, S. Oda, O. Vasilieva, M. Miyabe, S. Yoshida, T. Katagiri, Y. Aoki, T. Hoshida, and T. Ikeuchi, “Accurate prediction of quality of transmission with dynamically configurable optical impairment model,” in Optical Fiber Communications Conference (Optical Society of America, 2017), paper Th1J.4.
[Crossref]

Araki, S.

Barletta, L.

C. Rottondi, L. Barletta, A. Giusti, and M. Tornatore, “Machine-learning method for quality of transmission prediction of unestablished lightpaths,” J. Opt. Commun. Netw. 10(2), A286–A297 (2018).
[Crossref]

L. Barletta, A. Giusti, C. Rottondi, and M. Tornatore, “QoT estimation for unestablished lighpaths using machine learning,” in Optical Fiber Communications Conference (Optical Society of America, 2017), paper Th1J.1.
[Crossref]

Barry, R. A.

M. Medard, D. Marquis, R. A. Barry, and S. G. Finn, “Security issues in all-optical networks,” IEEE Netw. 11(3), 42–48 (1997).
[Crossref]

Belhumeur, P. N.

P. N. Belhumeur, J. P. Hespanha, and D. J. Kriegman, “Eigenfaces vs. fisherfaces: Recognition using class specific linear projection,” IEEE Trans. Pattern Anal. Mach. Intell. 19(7), 711–720 (1997).
[Crossref]

Bergman, K.

Bouda, M.

M. Bouda, S. Oda, O. Vasilieva, M. Miyabe, S. Yoshida, T. Katagiri, Y. Aoki, T. Hoshida, and T. Ikeuchi, “Accurate prediction of quality of transmission with dynamically configurable optical impairment model,” in Optical Fiber Communications Conference (Optical Society of America, 2017), paper Th1J.4.
[Crossref]

Cao, J.

Chapelle, O.

O. Chapelle, “Training a support vector machine in the primal,” Neural Comput. 19(5), 1155–1178 (2007).
[Crossref] [PubMed]

Chen, C.

Chen, J.

N. Skorin-Kapov, J. Chen, and L. Wosinska, “A new approach to optical networks security: Attack-aware routing and wavelength assignment,” IEEE/ACM Trans. Netw. 18(3), 750–760 (2010).
[Crossref]

Chen, L.

Cho, P. B.

Cugini, F.

S. Shahkarami, F. Musumeci, F. Cugini, and M. Tornatore, “Machine-learning-based soft-failure detection and identification in optical networks,” in Optical Fiber Communications Conference (Optical Society of America, 2018), paper M3A.5.
[Crossref]

Cui, S.

de Carvalho, L. H. H.

de Oliveira, J. C.

Deng, R.

Diniz, J.

Diniz, J. C. M.

Doberstein, A.

Estaran, J.

Fan, Q.

S. Yan, F. N. Khan, A. Mavromatis, D. Gkounis, Q. Fan, F. Ntavou, K. Nikolovgenis, F. Meng, E. H. Salas, C. Guo, C. Lu, A. P. T. Lau, R. Nejabati, and D. Simeonidou, “Field trial of machine-learning-assisted and SDN-based optical network planning with network-scale monitoring database,” in Proceedings of European Conference on Optical Communication (IEEE, 2017), 1–3.
[Crossref]

S. Yan, F. N. Khan, A. Mavromatis, Q. Fan, H. Frank, R. Nejabati, A. P. T. Lau, and D. Simeonidou, “Field trial of machine-learning-assisted and SDN-based optical network management,” in Optical Fiber Communication Conference (Optical Society of America, 2019), paper M2E.1.
[Crossref]

Finn, S. G.

M. Medard, D. Marquis, R. A. Barry, and S. G. Finn, “Security issues in all-optical networks,” IEEE Netw. 11(3), 42–48 (1997).
[Crossref]

Franciscangelis, C.

Frank, H.

S. Yan, F. N. Khan, A. Mavromatis, Q. Fan, H. Frank, R. Nejabati, A. P. T. Lau, and D. Simeonidou, “Field trial of machine-learning-assisted and SDN-based optical network management,” in Optical Fiber Communication Conference (Optical Society of America, 2019), paper M2E.1.
[Crossref]

Furdek, M.

N. Skorin-Kapov, M. Furdek, S. Zsigmond, and L. Wosinska, “Physical-layer security in evolving optical networks,” IEEE Commun. Mag. 54(8), 110–117 (2016).
[Crossref]

M. Furdek, N. Skorin-Kapov, S. Zsigmond, and L. Wosinska, “Vulnerabilities and security issues in optical networks,” in Proceedings of International Conference on Transparent Optical Networks (IEEE, 2014), 1–4.
[Crossref]

Gariépy, D.

Giusti, A.

C. Rottondi, L. Barletta, A. Giusti, and M. Tornatore, “Machine-learning method for quality of transmission prediction of unestablished lightpaths,” J. Opt. Commun. Netw. 10(2), A286–A297 (2018).
[Crossref]

L. Barletta, A. Giusti, C. Rottondi, and M. Tornatore, “QoT estimation for unestablished lighpaths using machine learning,” in Optical Fiber Communications Conference (Optical Society of America, 2017), paper Th1J.1.
[Crossref]

Gkounis, D.

S. Yan, F. N. Khan, A. Mavromatis, D. Gkounis, Q. Fan, F. Ntavou, K. Nikolovgenis, F. Meng, E. H. Salas, C. Guo, C. Lu, A. P. T. Lau, R. Nejabati, and D. Simeonidou, “Field trial of machine-learning-assisted and SDN-based optical network planning with network-scale monitoring database,” in Proceedings of European Conference on Optical Communication (IEEE, 2017), 1–3.
[Crossref]

Gonzalez, N. G.

Green, R. J.

R. Rejeb, M. S. Leeson, and R. J. Green, “Multiple attack localization and identification in all-optical networks,” Opt. Switching Networking 3(1), 41–49 (2006).
[Crossref]

Guo, C.

S. Yan, F. N. Khan, A. Mavromatis, D. Gkounis, Q. Fan, F. Ntavou, K. Nikolovgenis, F. Meng, E. H. Salas, C. Guo, C. Lu, A. P. T. Lau, R. Nejabati, and D. Simeonidou, “Field trial of machine-learning-assisted and SDN-based optical network planning with network-scale monitoring database,” in Proceedings of European Conference on Optical Communication (IEEE, 2017), 1–3.
[Crossref]

Gutterman, C. L.

Haisch, H.

Harasawa, K.

He, G.

He, J.

Hespanha, J. P.

P. N. Belhumeur, J. P. Hespanha, and D. J. Kriegman, “Eigenfaces vs. fisherfaces: Recognition using class specific linear projection,” IEEE Trans. Pattern Anal. Mach. Intell. 19(7), 711–720 (1997).
[Crossref]

Hoshida, T.

M. Bouda, S. Oda, O. Vasilieva, M. Miyabe, S. Yoshida, T. Katagiri, Y. Aoki, T. Hoshida, and T. Ikeuchi, “Accurate prediction of quality of transmission with dynamically configurable optical impairment model,” in Optical Fiber Communications Conference (Optical Society of America, 2017), paper Th1J.4.
[Crossref]

Hu, W.

Hua, N.

N. Li, N. Hua, Y. Yu, Q. Luo, and X. Zheng, “Light Source and Trail Recognition via Optical Spectrum Feature Analysis for Optical Network Security,” IEEE Commun. Lett. 22(5), 982–985 (2018).
[Crossref]

Y. Li, N. Hua, Y. Song, S. Li, and X. Zheng, “Fast lightpath hopping enabled by time synchronization for optical network security,” IEEE Commun. Lett. 20(1), 101–104 (2016).
[Crossref]

Y. Li, N. Hua, C. Zhao, H. Wang, R. Luo, and X. Zheng, “Real-Time Rogue ONU Identification with 1D-CNN-based Optical Spectrum Analysis for Secure PON,” in Optical Fiber Communication Conference (Optical Society of America, 2019), paper Tu3B.3.
[Crossref]

Huang, L.

Huang, Y.

Ikeuchi, T.

M. Bouda, S. Oda, O. Vasilieva, M. Miyabe, S. Yoshida, T. Katagiri, Y. Aoki, T. Hoshida, and T. Ikeuchi, “Accurate prediction of quality of transmission with dynamically configurable optical impairment model,” in Optical Fiber Communications Conference (Optical Society of America, 2017), paper Th1J.4.
[Crossref]

Inoue, K.

Jones, R.

Katagiri, T.

M. Bouda, S. Oda, O. Vasilieva, M. Miyabe, S. Yoshida, T. Katagiri, Y. Aoki, T. Hoshida, and T. Ikeuchi, “Accurate prediction of quality of transmission with dynamically configurable optical impairment model,” in Optical Fiber Communications Conference (Optical Society of America, 2017), paper Th1J.4.
[Crossref]

Ke, C.

Khan, F. N.

F. N. Khan, K. Zhong, X. Zhou, W. H. Al-Arashi, C. Yu, C. Lu, and A. P. T. Lau, “Joint OSNR monitoring and modulation format identification in digital coherent receivers using deep neural networks,” Opt. Express 25(15), 17767–17776 (2017).
[Crossref] [PubMed]

S. Yan, F. N. Khan, A. Mavromatis, D. Gkounis, Q. Fan, F. Ntavou, K. Nikolovgenis, F. Meng, E. H. Salas, C. Guo, C. Lu, A. P. T. Lau, R. Nejabati, and D. Simeonidou, “Field trial of machine-learning-assisted and SDN-based optical network planning with network-scale monitoring database,” in Proceedings of European Conference on Optical Communication (IEEE, 2017), 1–3.
[Crossref]

S. Yan, F. N. Khan, A. Mavromatis, Q. Fan, H. Frank, R. Nejabati, A. P. T. Lau, and D. Simeonidou, “Field trial of machine-learning-assisted and SDN-based optical network management,” in Optical Fiber Communication Conference (Optical Society of America, 2019), paper M2E.1.
[Crossref]

Kitayama, K. I.

Kriegman, D. J.

P. N. Belhumeur, J. P. Hespanha, and D. J. Kriegman, “Eigenfaces vs. fisherfaces: Recognition using class specific linear projection,” IEEE Trans. Pattern Anal. Mach. Intell. 19(7), 711–720 (1997).
[Crossref]

Lau, A. P. T.

F. N. Khan, K. Zhong, X. Zhou, W. H. Al-Arashi, C. Yu, C. Lu, and A. P. T. Lau, “Joint OSNR monitoring and modulation format identification in digital coherent receivers using deep neural networks,” Opt. Express 25(15), 17767–17776 (2017).
[Crossref] [PubMed]

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Figures (12)

Fig. 1
Fig. 1 (a) Masquerade attacker gain access to network incognito and insert signals; (b). Unauthorized users transmit unauthorized signals in authorized channels
Fig. 2
Fig. 2 (a) OS of reference transmitter; (b) Internal structure of the transmitters; (c–h) OS of simulated transmitters, each with one parameter differing from the reference as indicated.
Fig. 3
Fig. 3 The six groups of different transmission distances and conditions in the simulation.
Fig. 4
Fig. 4 Optical spectra of the reference transmitter under the six conditions shown in Fig. 3.
Fig. 5
Fig. 5 BWCDR calculated from OS samples with different transmission conditions corresponding to those shown in Fig. 3.
Fig. 6
Fig. 6 (a) Contributions of PCs as the number of PCs grows under different transmission conditions. (b) Distribution of 2450 OS samples in the first 3 PC spaces (colors represent different transmitters; different transmission conditions form clusters of points).
Fig. 7
Fig. 7 True samples, predicted samples, and the calculation of accuracy by FAR and MAR.
Fig. 8
Fig. 8 Recognition results with the SVM. (a) Recognition accuracy with different training samples as the number of PCs grows in the first experiment; (b–d) Accuracy, FAR, and MAR of authorized/unauthorized OS recognition for different number of PCs and different threshold of loss in the second experiment.
Fig. 9
Fig. 9 Recognition results with 1D-CNN: (a) Recognition accuracy with different training samples as the number of PCs grows in the first experiment; (b–d) Accuracy, FAR, and MAR of authorized/unauthorized OS recognition with different number of PCs and different threshold of loss in the second experiment.
Fig. 10
Fig. 10 (a) and (c) experimental setup, (b) experiment topology and transmission conditions, (d) optical spectra of SFP 1–8 light sources.
Fig. 11
Fig. 11 (a) Contributions of PCs as the number of PCs is increased from 1 to 10. (b) Distribution of 4800 OS samples in the first 3 PC spaces (colors represents the SFP and different transmission conditions form distinct clusters of points).
Fig. 12
Fig. 12 Recognition results with SVM (a, c, e) and 1D-CNN (b, d, f): accuracy, FAR, and MAR of authorized/unauthorized OS recognition with different numbers of PCs and thresholds of loss.

Equations (14)

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E out (t)= E in (t)cos π 2 V π [ V 1 (t) V 2 (t)] e j π 2 V π V 1 (t)+ V 2 (t)
E out (t)= E in (t)cos π 2 V π { V bias +2 V drive [ g(t)+N(t) ] } e j π 2 V π ( V bias1 + V bias2 )
P out (t)= E out (t) E out * (t)= P in (t) cos 2 π 2 V π { V bias +2 V drive [ g(t)+N(t) ] }
P out (ω)= 1 2 P in (ω) (a) + 1 4π P in (ω) (a) F{ cos π V π { V bias (b) +2 V drive (c) [ g(t) (d) + N(t) (e) ] } }
S w = i=1 N S wi = i=1 N x j X i ( x j μ i ) ( x j μ i ) T
S b = i=1 N N i ( μ i μ) ( μ i μ) T
BWCDR= tr( H T S B H) tr( H T S W H)
z i =( z i1 , z i2 ,..., z ik )= x i × W T
C k = i k λ i / j d λ j
f(x)= ω T ϕ(x)+b
Loss(i,c)= k g kic K = k ( max(0,1 y kic s kic ) ) 2K
Labe l i ={ SVM( x i ) min c (Loss(i,c))<Th unauthorized min c (Loss(i,c))>Th
los s CCE = 1 N i=1 N y ^ i log( y ^ i ) , i N y ^ i =1
Labe l i ={ CNN( x i ) los s CCE i <Th unauthorized los s CCE i >Th

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