Robust Spectrum Decision Protocol against Primary User Emulation Attacks in Dynamic Spectrum Access Networks

Transcription

1 Robust Spectu Decision Potocol against Piay Use Eulation Attacks in Dynaic Spectu Access Netwoks Z. Jin, S. Anand and K. P. Subbalakshi Depatent of Electical and Copute Engineeing Stevens Institute of Technology, New Jesey, USA Eail: {zjin, asanthan, Abstact We popose a spectu decision potocol esilient to piay use eulation attacks PUEA in dynaic spectu access netwoks. PUEA is a type of denial-of-sevice attack that can seveely intefee with the spectu sensing pocess and unfaily depive legitiate seconday uses of spectu access. In this pape, we pesent a obust spectu decision potocol that can itigate PUEA using individual spectu decisions ade by seconday nodes in the netwok. In ode to enable each seconday node to ake an individual spectu decision to detect PUEA, we fist chaacteize the eceived powe at good seconday use. This is done by using a flexible log-noal su appoxiation. The eceived powe thus chaacteized is used to deteine the pobability of successful PUEA on each seconday use, which is used to develop the poposed potocol. Siulation esults deonstate that the poposed potocol can significantly educe the pobability of successful PUEA unde Byzantine attacks i.e., when the alicious uses intentionally povide false spectu decisions, while still following the spectu evacuation etiquette. Keywods Dynaic spectu access DSA, piay use eulation attack PUEA, spectu decision potocol I. INTRODUCTION Dynaic spectu access DSA netwoks have eceived extensive attention ecently because of thei ability to bette seve the gowing bandwidth deands of the uses. This is achieved by allowing unlicensed seconday uses to access spectu bands when the licensed piay uses do not use these bands. Seconday uses continuously sense the spectu and follow a spectu evacuation etiquette to evacuate the band, upon the etun of the piay use. This spectu evacuation potocol, howeve, can be anipulated by alicious uses, by ticking the syste into believing that thee is a piay use, when none is pesent. This can be done in vaious ways, one of which is to eulate the signal chaacteistics of the piay. Thus, the good seconday uses following the noal spectu evacuation pocess evacuate the spectu unnecessaily, leading to what is known as piay use eulation attack PUEA [1. Seveal ethods exist to thwat PUEA and can be classified into location awae [ and location unawae techniques [3,[4. Typically, location awae techniques involve significant infastuctue ovehead like a dedicated senso netwok to deteine the locations of tansittes [. We ecently poposed a Wald s sequential pobability atio test fo individual seconday uses to detect PUEA in [3 and extended the analysis in [4 to include a Neyan-Peason coposite hypothesis test as an altenative. The netwok ay also use a centalized decision ule whee individual secondaies tansit data concening piay activity to a centalized contolle 1 which then akes the final decision on PUEA s pesence o opeate in a non-centalized anne whee each seconday use akes its own decision about the pesence of PUEA to detect PUEA. If designed well, the pobability of successful PUEA can be significantly educed in the centalized odel. In this pape, we popose a obust spectu decision potocol fo DSA netwoks with centalized contolle which uses the individual sensing esults of seconday uses to ake the final spectu decision fo the entie netwok. We fist use a flexible log-noal su appoxiation to chaacteize the eceived powe at good seconday use. We then popose an individual detection echanis fo seconday uses to achieve individual sensing esults. The pobability of successful PUEA at each good use is then deived to analyze the effect of PUEA on the whole netwok, in tes of the nube of good uses successfully attacked by the alicious uses. A obust spectu decision potocol, in which a centalized contolle collects individual sensing esults fo seconday uses and akes the final spectu decision fo the entie netwok, is then developed to defend the netwok against PUEA. We also take into account that in addition to launching PUEA, alicious uses could also launch Byzantine attacks [5 by sending false sensing esults to the centalized contolle. We show by siulations that the poposed potocol can effectively itigate PUEA unde Byzantine attacks while still following the spectu evacuation etiquette. The est of the pape is oganized as follows. In Section II, we pesent the syste odel. In Section III, we popose the obust spectu decision potocol that is esilient to PUEA. Nueical esults ae pesented in Section IV. Section V povides the conclusion. 1 The centalized contolle can be, e.g., a base station in IEEE 80. WRANs, o a tustwothy seconday use in ad-hoc DSA netwoks.

2 II. SYSTEM MODEL We conside a scenaio whee all seconday uses, i.e., all the good uses and the alicious uses, ae distibuted in a squae gid as shown in Fig. 1. A piay use is located at a distance d p fo the cente of the squae gid. Spectu sensing of seconday use is based on enegy detection, i.e., a seconday use copaes its eceived powe to soe pe-defined thesholds. If the eceived powe is below the detection sensitivity, then the spectu band is consideed to be vacant. Othewise, the spectu band could contain the signal tansitted by the piay tansitte, o the signal tansitted by the set of alicious uses with the intent to launch PUEA. The good uses, then have to deteine whethe the cuent tansission is fo the piay use o fo the alicious uses. Ou objective in this pape is two fold. 1 Develop a detection echanis to enable each individual node to ake a spectu decision i.e., deteine whethe the eceived signal is fo the piay tansitte o PUEA. Develop a centalized spectu decision potocol, in which a centalized contolle gathes the spectu decisions ade by each seconday node, to pefo a coon spectu decision fo all the nodes, to detect PUEA. In ode to achieve objective 1 entioned above, it is desied to analyze the eceived powe at each seconday node, due to tansission by the piay tansitte, as well as due to tansission by the alicious uses. The following assuptions ae ade to cay out ou analysis. R0 L dp Good Seconday Use Malicious Seconday Use Piay Tansitte Fig. 1. A dynaic spectu access netwok with length L, consisting of good seconday uses and alicious seconday uses. No alicious uses ae pesent within a adius R 0 about each good use. A piay tansitte is located at a distance d p fo the cente of the gid. 1 Thee ae N g good uses and N alicious uses, spatially Poisson distibuted with paaetes g and, espectively, in a squae gid of length L. The positions of the good uses and the alicious uses ae statistically independent of each othe. 3 Fo the i th good use located at x i, y i, no alicious uses ae pesent within a cicle of adius R 0 centeed at x i, y i. R 0 is the exclusive distance fo the seconday use [4. 4 The piay use tansits at a powe of P t and the alicious uses tansit at a powe of P. 5 The path loss exponent fo the popagation fo the piay use to any good use is while that fo any alicious use to any good use is 4 [4. 6 The RF signals also undego log-noal shadowing. The shadowing at any good use both fo the piay use and fo any alicious use, denoted by G p and G, ae log-noally distibuted espectively, i.e., 10 log 10 G p N 0, σ p and 10 log10 G N 0, σ, whee Nµ, σ denotes a noal distibution with ean µ and vaiance σ. 7 The Rayleigh fading is assued to be aveaged out and can hence be ignoed [4. III. PROTOCOL TO MITIGATE PRIMARY USER EMULATION ATTACKS Befoe developing the potocol esilient to PUEA, we pesent the analysis to obtain the vaious paaetes equied to develop the potocol. We fist chaacteize the eceived powe at each good seconday use in Section III-A. We then use this to obtain the pobability density function pdf of the eceived powe in Section III-B. In Section III-C, an individual detection echanis fo seconday uses is poposed and its effect on the entie netwok is studied. The paaetes thus deteined ae finally used to develop the potocol in Section III-D. A. Received Powe at Good Use Since the piay use is usually fa away fo the seconday netwok, its distance to any good use can be appoxiated by d p. Thus the eceived powe at any good use fo the piay, P p, can be witten as P p = P t d p G p. 1 Fo any good use, its eceived powe fo j th alicious neighbo, P j, can be witten as P j = P d j 4 G, whee d j is the distance fo the good use to its j th alicious neighbo theefoe, d 1 d d N. The total eceived powe fo all N alicious uses, P, can be obtained as P = N j=1 P j. 3 Typically, the powe eceived at a seconday use fo its fist two alicious neighbos is uch lage than the su of the powe eceived fo all the othe alicious neighbos. This is because, d j >> d i, fo j > and i = 1,, and hence, d j 4, fo j >, is negligible. Theefoe, we only conside the powe eceived fo the fist two alicious neighbos. Thus, P can be appoxiated by whee P 1 P P 1 + P, 4 and P can be obtained fo Eqn.. This appoxiation will be justified in Fig. in Section IV.

3 Since the good uses and the alicious uses ae both spatially Poisson distibuted, and thei locations ae independent of each othe, all N = N g + N secondaies including both good and alicious uses ae also spatially Poisson distibuted with paaete = g +. The pdf of the distance between any good use and its n th neighbo, d n, is given by [6, Theoe 1 f n d n = e πd n πd n n d n Γn, 5 whee Γ is the genealized Gaa function. Fo Eqn. 3, the total eceived powe fo all N alicious uses depends on d j 4, j, which, in tun, is deteined by the location of the good use and the location of the alicious use. Since all locations ae ando, coputation of P is coplex. Theefoe, we use E[d j 4 to siplify analysis. Since the positions of the seconday uses good and alicious ae independent, the j th alicious neighbo of a good use is the n th neighbo j n N 1 with pobability n 1 j 1 j g n j. Theefoe, E[d j 4 is given by N 1 E[d j 4 = E[d n 4 n 1 j 1 n=j j g n j, 6 which can be futhe siplified by appoxiating N by E[N. In Eqn. 6, E[d n 4 can be obtained by using Eqn. 5. P j is thus obtained by substituting Eqn. 6 in Eqn., and then P can be calculated fo Eqn. 4. B. Pobability Density Function of Received Powe Since d p and P t ae fixed, P p is log-noally distibuted, i.e., 10 log 10 P p N µ p, σp, whee µp is given by µ p = 10 log 10 P t 0 log 10 d p. 7 Siilaly, conditioned on E[d j 4 and P, evey te of the ight hand side of Eqn. 3 is also log-noally distibuted, thus, P can be appoxiated as a log-noal ando vaiable RV. To copute the statistical paaetes of P, we adopt the flexible log-noal su appoxiation ethod poposed by Wu et al in [7. Thus, conditioned on the distance to the 1 st alicious neighbo and P, P 1 is log-noally distibuted, i.e., 10 log 10 P 1 N µ 1, σ. Since the distance to the 1 st alicious neighbo is appoxiated by E[d 1 4, µ 1 is given by µ 1 = 10 log 10 P + 10 log 10 E[d Siilaly, 10 log 10 P N µ, σ, whee µ is given by P µ = 10 log 10 P + 10 log 10 E[d 4. 9 is then odeled as a log-noal RV, i.e., 10 log 10 P N µ M, σm. The expessions fo µ 1 and µ in Eqns. 8 and 9 ae used to copute µ M and σ M by using the technique descibed in [7. C. Individual Detection Mechanis fo Good Use Cuently, no policy on spectu sensing has incopoated any counte-easue to PUEA. This howeve, is not ecoended fo good uses to sense the piay tansission i.e., based only on the detection sensitivity, because even a sall nube of alicious uses can tansit enough powe to ake the eceived powe at good use exceed the detection sensitivity usually -94dB, thus esulting in successful PUEA all the tie. In this subsection, we popose an individual detection echanis fo good use, with the goal of achieving odeate PUEA esilience while not copoising the sensitivity equied to detect the etun of the piay use. The poposed detection echanis is then incopoated into the spectu decision potocol that will be poposed in Section III-D, to futhe itigate PUEA. Since 10 log 10 P p Nµ p, σp with µ p given by Eqn. 7, we popose to use the epiical ule of noal distibution to detect the piay use, which is as follows. The eceived powe fo the piay use epesented in decibels db, P p db, is ost likely to satisfy µ p 3σ p P p db µ p + 3σ p. Theefoe, by setting the detection thesholds as µ p 3σ p and µ p +3σ p, the pobability of issing the piay i.e., failing to detect the pesence of piay use, p iss, is given by p iss = 1 P { µ p 3σ p P p db µ p + 3σ p } = 1 Q 3 + Q 3 = , 10 whee Q is the Q-function defined by Qx = 1 π exp t dt. Thus, the pobability of successful x PUEA, p PUEA, is given by { } p PUEA = P µ p 3σ p P db µ p + 3σ p µp 3σ p µ M = Q σ M µp + 3σ p µ M Q. 11 σ M Let K denote the nube of good uses attacked by the alicious uses and K denote the nube of good uses that iss the piay. Since the pobability of successful PUEA on any good use is independent of that on any othe good use and the nube of good uses is a Poisson RV, the set of attacked uses can be obtained by splitting the nube of good uses, N g, accoding to a Benoulli pocess with pobability of success, p PUEA. Thus K is a Poisson RV with paaete E[N g p PUEA. Siilaly, K is a Poisson RV with paaete E[N g p iss. Hence, the expected nube of attacked uses, µ K, and the vaiance of the nube of attacked uses, σ K, ae given by µ K = σ K = E [N g p PUEA. 1 Siilaly, the expected nube of good uses issing the piay, µ K, and the vaiance of the nube of good uses

4 issing the piay, σk, ae given by µ K = σ K = E [N g p iss. 13 The values of µ K, σ K, µ K and σ K can be used to obtain a spectu decision potocol to itigate PUEA, as will be explained in the following subsection. D. Spectu Decision Potocol against Piay Use Eulation Attacks We now develop the spectu decision potocol esilient to PUEA, in which a centalized contolle obtains the individual spectu decisions ade accoding to the discussion in Section III-C, fo all the seconday uses. The basic idea behind the potocol is as follows. Fo the values of µ K and σ K given by Eqn. 1, it is possible to estiate the nube of seconday uses who sense piay tansission when PUEA is launched. This set of uses also includes the alicious uses who launch Byzantine attacks, i.e., spuiously clai piay tansission while launching PUEA. Siilaly, fo µ K and σ K given by Eqn. 13, we can estiate the nube of seconday uses who would successfully detect piay tansission when the piay use tansits. It is noted that when piay tansission takes place, the alicious uses do not gain anything by launching Byzantine attacks and hence, will povide coect infoation to the centalized contolle. Since the individual detection echanis detects PUEA to soe extent and hadly isses the piay use, one can expect oe uses claiing piay tansission when the piay use tansits than when PUEA is launched. Theefoe by setting appopiate thesholds on the nube of uses whose individual detection indicates piay tansission, the centalized contolle can itigate PUEA. The detailed desciption of the potocol is as follows. 1 Each individual seconday use senses the spectu and sends its sensing esult to the centalized contolle based on the individual detection echanis poposed in Section III-C, i.e., a if the eceived powe in db is in the ange [µ p 3σ p, µ p + 3σ p, the seconday use clais that the piay tansission is detected, b else, it clais that PUEA is detected. The centalized contolle deteines whethe the ongoing tansission is fo the piay use o due to PUEA, based on the following citeia, a if the nube of sensing esults claiing piay tansission, denoted by N p, is geate than N u = E [N g µ K Aσ K +E [N, the centalized contolle deteines that the tansission is fo the piay use, b else if N p < N l = µ K + Bσ K + E [N, the centalized contolle decides that the alicious uses ae launching PUEA, c else i.e., when N l N p N u, the centalized contolle concludes piay tansission with pobability Np N l N u N l o PUEA with pobability N u N p N u N l. 3 Afte the good uses eceive the spectu decision sent by the centalized contolle, they a vacate the spectu if the centalized contolle decides that it is piay tansission, b else, continue using the spectu if the centalized contolle decides that it is PUEA. The values of A and B ae chosen to educe the gap between N u and N l, and to aintain N u > N l. The choice of the actual values is epiical. IV. NUMERICAL RESULTS The values of the syste paaetes used in siulations ae listed in Table I. Paaete Value Paaete Value d p 100 K P t 100 KW L 000 P 4 W E [N g 00 σ p 8 db R 0 30 σ 5.5 db A 3 B 1 TABLE I VALUES OF PARAMETERS USED IN SIMULATIONS. Fig. shows the copaison of the eceived powe at any good use fo its fist two alicious neighbos and that fo all the othe alicious neighbos. It can be seen that the eceived powe fo the fist two alicious neighbos is about one to thee odes of agnitude geate than that fo all the othe alicious neighbos, thus justifying the appoxiation in Eqn. 4. Received powe fo alicious uses, P W Theoetical, 1 st two Expeiental, 1 st two Theoetical, all othes Expeiental, all othes 10 7 Fig.. Copaison of eceived powe at good use fo its fist two alicious neighbos, and that fo all the othe alicious neighbos. Fig. 3 pesents the detection eo in tes of the pobability of successful PUEA and the pobability of issing the piay use when good uses ake individual spectu decisions, based on the detection echanis poposed in Section III-C. It is obseved that the theoetical esults closely follow the expeiental esults, thus validating the analysis pesented in Sections III-A, III-B and III-C. It can be seen fo Fig. 3a that although soeties PUEA ay be detected easily by soe individual nodes, by deploying the poposed individual

5 detection echanis, the netwok as a whole, still suffes with the pobability of successful PUEA as high as about 0.7. This bings foth, the need fo additional secuity enhanceents to futhe itigate PUEA. It can also be seen that the pobability of successful PUEA inceases as the nube of alicious uses inceases, indicating that oe good uses will be attacked if the alicious uses accuulate oe tansitting powe. Note that in Fig. 3b, the pobability of issing the piay use is fixed, which follows fo Eqn Pobability of successful PUEA, p PUEA Potocol Individual 10 5 Pobability of successful PUEA, p PUEA Pobability of issing piay use, p iss.5.6 a Pobability of successful PUEA. Theoetical Expeiental a Pobability of successful PUEA..7 Theoetical Expeiental b Pobability of issing the piay. Fig. 3. Detection eo when good uses use the individual detection echanis poposed in Section III-C. Fig. 4 pesents the copaison on detection eo between the potocol descibed in Section III-D and the individual detection echanis poposed in Section III-C, unde Byzantine attacks. Fo Fig. 4a we obseve that the pobability of successful PUEA can be significantly educed afte ipleenting the poposed potocol. Fo exaple, fo E[N = 50, the poposed potocol esults in a pobability of successful PUEA of 1.6, as against the pobability of 0.4 if nodes ely only on the individual detection echanis. Fo E[N = 100, a eduction of 66.5% on the pobability of successful PUEA can be achieved when the poposed potocol is used. It is also shown in Fig. 4b that although the poposed potocol esults in a highe pobability of issing the piay use, the actual values of the pobability neve exceed Theefoe, the poposed potocol can successfully defend the netwok against PUEA in the pesence of Byzantine attacks while still following the spectu evacuation etiquette. V. CONCLUSION We pesented a centalized spectu decision potocol fo itigating PUEA in DSA netwoks. The poposed potocol ade use of the individual spectu decision ade by each seconday use. The individual spectu decision was obtained by chaacteizing the eceived powe at good seconday use though a flexible log-noal su appoxiation ethod. The poposed potocol was esilient to PUEA and esulted in a significantly educed pobability of successful PUEA in the pesence of Byzantine attacks, while still following the spectu evacuation etiquette. The extension of the poposed idea to obtain distibuted spectu decision potocols, is unde investigation. Pobability of issing piay use, p iss Potocol Individual b Pobability of issing the piay. Fig. 4. Copaison on detection eo between the potocol descibed in Section III-D and the individual detection echanis poposed in Section III- C, unde Byzantine attacks. ACKNOWLEDGMENT This eseach was patially funded by NSF # and NSF # and patially funded by NJ-IJ. REFERENCES [1 R. Chen and J. M. Pak, Ensuing tustwothy spectu sensing in cognitive adio netwoks, Poc., IEEE Wokshop on Netwoking Technol. fo Softwae Defined Radio Netwoks, pp , Sep [ R. Chen, J. M. Pak, and J. H. Reed, Defense against piay use eulation attacks in cognitive adio netwoks, IEEE Jl. on Sel. Aeas in Coun.: Spl. Issue on Cognitive Radio Theoy and Applications, vol. 6, no. 1, pp. 5 37, Jan [3 Z. Jin, S. Anand, and K. P. Subbalakshi, Detecting piay use eulation attacks in dynaic spectu access netwoks, Poc., IEEE Intl. Conf. on Coun. ICC 009, Jun [4, Mitigating piay use eulation attacks in dynaic spectu access netwoks using hypothesis testing, ACM SIGMOBILE Mobile Coputing and Coun. Review, Spl. Issue on Cognitive Radio Technol. and Sys., vol. 13, no., pp , Apil 009. [5 L. Lapot, R. Shostak, and M. Pease, The Byzantine geneals poble, ACM Tans. on Pog. Languages and Sys., vol. 4, no. 3, pp , Jul [6 M. Haenggi, On distances in unifoly ando netwoks, IEEE Tans. on Info. Theoy, vol. 51, no. 10, pp , Oct [7 J. Wu, N. B. Mehta, and J. Zhang, A flexible lognoal su appoxiation ethod, Poc., IEEE Global Coun. Conf. GLOBECOM 005, vol. 6, pp , Dec. 005.