Abstract
In this paper, we discuss the physical layer security from a new angle of view and propose a novel approach to resist some attacks in signal processing perspective. The scenario considered in this paper is that the eavesdropper has the similar channel performance compared to the legitimate receiver. We design the optimal artificial noise (AN) to resist the attacks of the eavesdropper who uses the blind source separation (BSS) technology to reconstruct the secret information. For speech signals, the optimal AN is obtained by minimizing the maximum of the correlation coefficients between the source signal and the received signals at Eve and the correlation coefficients between the source signal and separated results of BSS. For binary phase shift keying (BPSK) signals, we maximize the minimum bit error rates (BERs) of the separated signals and the obtained signals at Eve. Moreover, we consider the AN design from the point of breaking the BSS conditions, and propose a method by changing the correlation coefficient randomly. The simulation results show that the AN we proposed has better performance than that of the white Gaussian AN to resist the BSS attacks for both speech signals and the BPSK signals.
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References
Bloch M, Barros J, Rodrigues MRD et al (2008) Wireless information theoretic security. IEEE Trans Inf Theory 54:2515–2534
Kavitha T, Sridharan D (2010) Security vulnerabilities in wireless sensor networks: a survey. J Inf Assur Secur 5:31–44
Perrig A, Szewczyk R, Tygar JD et al (2002) SPINS: security protocols for sensor networks. Wirel Netw 8:521–534
Jolly G, Kuscu M, Kokate P et al (2003) A low-energy key management protocol for wireless sensor networks. Paper presented at IEEE international symposium on computers and communication, Kiris-Kemer, Turkey, 30 June–3 July 2003
Pinto PC, Barros J, Win MZ (2009) Wireless physical-layer security: the case of colluding eavesdroppers. In: IEEE (eds) Proceedings of IEEE international symposium on information theory, Seoul, Korea, 28 June–3 July 2009. IEEE, New York, pp 2442–2446
Dong L, Han Z, Petropulu AP et al (2009) Cooperative jamming for wireless physical layer security. Paper presented at IEEE/SP 15th workshop on statistical signal processing, Cardiff, United Kingdom, 31 August–3 September 2009
Goel S, Negi R (2008) Guaranteeing secrecy using artificial noise. IEEE Trans Wirel Commun 7:2180–2189
Zhou X, McKay MR (2010) Secure transmission with artificial noise over fading channels: achievable rate and optimal power allocation. IEEE Trans Veh Technol 59:3831–3842
Tekin E, Yener A (2008) The general Gaussian multiple-access and two-way wiretap channels: achievable rates and cooperative jamming. IEEE Trans Inf Theory 54:2735–2751
Tang X, Liu R, Spasojevic P et al (2011) Interference assisted secret communication. IEEE Trans Inf Theory 57:3153–3167
Zhu J, Mo J, Tao M (2010) Cooperative secret communication with artificial noise in symmetric interference channel. IEEE Commun Lett 14:885–887
Liao WC, Chang TH, Ma WK et al (2010) Joint transmit beamforming and artificial noise design for QoS discrimination in wireless downlink. In: Proceedings of IEEE international conference on acoustics speech and signal processing, Dallas, Texas, USA, 14–19 March, 2010. IEEE, Dallas, pp 2562–2565
Qin H, Chen X, Sun Y et al (2011) Optimal power allocation for joint beamforming and artificial noise design in secure wireless communications. In: Proceedings of IEEE 2011 ICC—2011 international conference on communications workshops, Kyoto, Japan, 5–9 June, 2011. IEEE, Piscataway
Lai L, El Gamal H, Poor HV (2008) The wiretap channel with feedback: encryption over the channel. IEEE Trans Inf Theory 54:5059–5067
Li W, Ghogho M, Chen B et al (2012) Secure communication via sending artificial noise by the receiver: outage secrecy capacity/region analysis. IEEE Commun Lett 16:1628–1631
Hyvärinen A, Oja E (2000) Independent component analysis: algorithms and applications. Neural Netw 13:411–430
Cao XR, Liu R (1996) General approach to blind source separation. IEEE Trans Signal Process 44:562–571
Tanaka A, Imai H, Miyakoshi M (2006) Theoretical foundations of second-order-statistics-based blind source separation for non-stationary sources. In: Proceedings of IEEE international conference on acoustics speech and signal processing, Toulouse, France, 14–19 May, 2006. IEEE, Piscataway, pp 600–603
Murata N, Ikeda S, Ziehe A (2001) An approach to blind source separation based on temporal structure of speech signals. Neurocomputing 41:1–24
Anderson M, Adali T, Li XL (2012) Joint blind source separation with multivariate Gaussian model: algorithms and performance analysis. IEEE Trans Signal Process 60:1672–1683
Kociński J, Drgas S, Ozimek E (2012) Evaluation of Blind Source Separation for different algorithms based on second order statistics and different spatial configurations of directional microphones. Appl Acoust 73:109–116
Lio G, Boulinguez P (2012) Greater robustness of second order statistics than higher order statistics algorithms to distortions of the mixing matrix in blind source separation of human EEG: implications for single-subject and group analyses. Neuroimage 67:137–152
Acknowledgment
This work was supported by the National “Twelfth Five-Year” Project of China (2012BAH38B05).
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The authors declare that they have no conflict of interest.
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SPECIAL TOPIC: Network and Information Security
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Li, G., Hu, A. & Huang, Y. A novel artificial noise aided security scheme to resist blind source separation attacks. Chin. Sci. Bull. 59, 4225–4234 (2014). https://doi.org/10.1007/s11434-014-0475-3
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DOI: https://doi.org/10.1007/s11434-014-0475-3