1. Physical Layer Authentication for Mobile Terminals over MIMO Fading Wiretap Channels. Mahendra Kumar Shukla(2011-DC-07) (mahendra.gla@gmail.com) December 11,2012

2. www.iiitm.ac.in 1. INTRODUCTION 2. MOTIVATION 3. LITERATURE REVIEW 4. RESEARCH GAP 5. OBJECTIVES 6. METHODOLOGY 7. WORK DONE SO FAR 8. RESULTS 9. REFERENCES CONTENTS

3. INTRODUCTION Wireless networks have become an essential part of our daily life. People rely heavily on wireless networks for transmission of important/private information. Security is a critical issue in wireless applications. Precise channel estimation allows authenticating the source and protecting the integrity of a message at the physical layer [4] without the need of a pre-shared secret key.

4. Continued…….. By doing security at physical layer we can reduce the burden on the authentication protocols at higher layer. Hypothesis testing [3] is one way by which we can develop an authentication scheme.

5. MOTIVATION Security is viewed as an independent feature addressed above the physical layer, and all widely used cryptographic protocols.. However, with the emergence of ad-hoc and decentralized networks, higher-layer techniques, such as encryption, are complex and difficult to implement. Therefore, there has been a considerable recent attention on studying the fundamental ability of the physical layer to provide secure wireless communications..

6. Literature Review Wiretap channels [5]

7. Continued…… Using the traditional terminology in the security literature, we consider three different agents: Alice, Bob and Eve. The authentication channel model [1]

8. Continued……. Authentication based on channel estimation [1]: Phase I:-Alice sends to Bob a training sequence, that we assume perfectly authenticated, for instance, by a cryptographic authentication at the application layer. ˆh(AB) = h(AB) + w(I)

9. Continued……. Phase II: Bob receives data packets. Authentication of these data packet is done in this phase. Now we apply hypothesis test H 0 : packet is from Alice, ˆh(t) = h(AB) + w(II)(t)

10. Continued……. H 1 : packet is not from Alice ˆh(t) = g(t) + w(II)(t) Now there may be two types of error can occur a. False alarm (FA) b.Missed detection (MD) Bob does not know the statistic of g. Therefore we apply GLRT.

11. Continued……. Flat Fading is caused by absorbers between the two antennae and is countered by antenna placement and transmit power level. The GLRT [7] consists in comparing the LLR with a threshold θ > 0. Ψ ≤ θ : decide for H0. Ψ > θ : decide for H1. We formulate the optimal attack strategy. The success probability of the attack is maximized by choosing ‘g’ to obtain the highest probability that the channel estimated by Bob lies in the sphere.

12. RESEARCH GAP Base paper scheme is only applicable for stationary legitimate transmitter. For mobile terminal it is not applicable. On the other hand, other attacks( such as jamming) are not considered.

13. OBJECTIVE Flat Fading is caused by absorbers between the two antennae and is countered by antenna placement and transmit power level. Our objective is to develop a scheme to authenticate the legitimate user in mobile environment. To evaluate the performance of the proposed method in MIMO/OFDM scenario.

14. Methodology We will start with the GLRT test. For this we will first derive LLR. Base paper result is only applicable for stationary Alice. Now we are assuming that Alice is moving with maximum velocity Va. To approach this we will take the help of [6] where authentication scheme is developed using NP Test.

15. Work done so far Literature Review of different Physical Layer Security methods. Derivation of LLR by applying GLRT assuming Alice is moving. Base paper has been simulated. Study of different attack strategies and derivation of single attack strategy.

16. Parameter Specification Uniform power delay profile is considered, i.e., λn = 1, n = 0, 1,...,N − 1. We focus first on the case where only one channel between h(AE) and h(EB) provides useful information to Eve, meaning that Eve performs an attack better than a simple guessing of ˆh (AB) only if she is sufficiently near to Alice or Bob. We impose ρ(AB) → 0, ρ(EB) → 0, ρ(AE) = ρ > 0.

17. Here we are considering OFDM system with N subcarriers. Here SNR(I)=15dB, SNR(II)=Infinity Results

18. Comparison between the developed authentication scheme and the outer bound. Here SNR(I)=15dB, SNR(II)=Infinity, N=5 Results

19. Here SNR(I)=15dB, SNR(II)=20 dB and Probability of false Alarm(Pfa)=10^-4. Results

20. To show the effectiveness of Eve’s Attack, we compare the average MD probability with the optimal strategy. Here SNR(I)=15dB, SNR(II)=Infinity and Probability of false Alarm(Pfa)=10^-4. Results

21. Continued…… Using the traditional terminology in the security literature, we consider three different agents: Alice, Bob and Eve. The authentication channel model [1]

22. SNR(II) =20 dB and Probability of false Alarm=10^-4. Results

23. Average MD probability versus Alpha in the MIMO scenario. Here N=2, SNR(II) =25 dB and \Rho=0.8 Results

24. References [1] P. Baracca, N. Laurenti, S. Tomasin, “Physical layer authentication over MIMO Fading Wiretap Channels,” IEEE Trans. Wireless Commun., vol. 11, no. 7, pp. 2564 - 2573, July. 2012. [2] L. Xiao, A. Reznik, W. Trappe, C. Ye, Y. Shah, L. J. Greenstein, and N. B. Mandayam, “PHY-authentication protocol for spoofing detection in wireless networks,” in Proc. 2010 IEEE Global Telecommun. Conf.,pp. 1–6. [3] U. M. Maurer, “Authentication theory and hypothesis testing,” IEEE Trans. Inf. Theory, vol. 46, no. 4, pp. 1350–1356, July 2000.

25. www.iiitm.ac.in [4] P. L. Yu, J. S. Baras, and B. M. Sadler, “Physical-layer authentication,” IEEE Trans. Inf. Forensics and Security, vol. 3, no. 1, pp. 38–51, Mar.2008. [5] A. Thangaraj, S. Dihidar, A. R. Calderbank, S. W. McLaughlin, and J. M. Merolla, “Applications of LDPC codes to the wiretap channels,”IEEE Trans. Inf. Theory, vol. 53, no. 8, pp. 2933–2945, Aug. 2007. [6] L. Xiao, L. J. Greenstein, N. B. Mandayam, and W.Trappe, “A physical layer technique to enhance authentication for mobile terminals,” in Proc.2008 IEEE Int. Conf. on Commun. [7] Mourad Barkat,”Statistical Decision Theory,” in Signal Detection and Estimation, 2nd ed. London: Artech House Publishers, 2005.

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