Mitigating Primary User Emulation Attacks in Dynamic Spectrum Access Networks using Hypothesis Testing

Transcription

1 Mitigating Pimay Use Emulation Attacks in Dynamic Spectum Access Netwoks using Hypothesis Testing Z. Jin S. An K. P. Subbalakshmi Depatment of Electical Compute Engineeing Stevens Institute of Technology, Hoboken, New Jesey, USA We pesent a Neyman-Peason composite hypothesis test (NPCHT) a Wald s sequential pobability atio test (WSPRT) to detect pimay use emulation attacks (PUEA) in cognitive adio netwoks. Most appoaches in the liteatue on PUEA assume the pesence of undelying senso netwoks fo localization of the malicious nodes. Thee ae no analytical studies available in the liteatue to study PUEA in the pesence of multiple malicious uses in fading wieless envionments. We pesent an NPCHT WSPRT based analysis to detect PUEA in fading wieless channels in the pesence of multiple omly located malicious uses. We show that thee is a ange of netwok adii in which PUEA ae most successful. Results also show that fo the same desied theshold on the pobability of missing the pimay, WSPRT can achieve a pobability of successful PUEA 5% less than that obtained by NPCHT. I. Intoduction Taditionally, adio spectum bs have been assigned to license holdes o sevices on a long tem basis fo lage geogaphical egions. This fixed spectum assignment policy has led to unde-utilization of the available spectum. The inefficiency in spectum usage the limited availability of spectum have given ise to cognitive adio enabled dynamic spectum access (DSA) as a new communication paadigm [], [], []. Seconday nodes in a DSA netwoks can use the licensed spectum bs when it is idle, unde the condition that they vacate it upon the etun of the pimay licensed uses (incumbent, pimay uses). In the est of the pape, we use the tem pimay o incumbent to efe to the licensed, high pioity use the tem seconday to denote the unlicensed uses. One example of cognitive adio netwoks (CRN) is the usage of unused spectum in the TV b. The TV tansmitte eceives ae pimay uses who ae licensed to use these bs. Othe uses who access the white spaces in the TV b on an ad-hoc basis ae temed seconday uses. The IEEE 8. woking goup on wieless egional aea netwoks [] povides the physical laye medium access contol specifications fo usage of the TV white spaces. The FCC s mated spectum policy efom [5] has esulted in a geat deal of eseach activities on vaious aspects of CRN including spectum sens- This wok was suppoted in pat by NSF Cybe Tust Gant No ing management, netwok achitectues, capacity, codes, tansmission techniques, spectum etiquette evacuation potocols as well as test-bed development. Stadization effots fo DSA netwoks include the IEEE Stads Coodinating Committee (IEEE SCC) s sponsoed pojects as well as IEEE 8. [6]. Spectum sensing in DSA is essential both fo identification of empty spectal bs (white spaces) as well as fo pompt evacuation upon the etun of incumbent. Potocols fo sensing pimay tansmission spectum evacuation can be found in [7], [8]. Pimay tansmitte detection techniques include enegy detection, cyclostationay featue detection matched filte detection []. Among these, enegy based detection is geneally moe popula due to ease of implementation. Despite the body of wok on othe aspects of CRN, eseach on secuity issues is still in its nascence [9]- [7]. In the paticula case of DSA netwoks, it can be agued that in ode to stage a denial-of-sevice (DoS) attack at the sensing level, it is necessay to affect the decision on pimay activity duing the sensing phase. This can be done in one of the following ways: (a) some malicious nodes can tansmit spuious signals that emulate the pimay use pimay use emulation attacks (PUEA) [], [], [6], [7]; (b) the spectum sensing nodes can lie about the spectum data (Byzantine attack) []; (c) by making use of the weaknesses of existing potocols fo evacuation [9] o (d) by modifying messages passed between the sens-

2 ing nodes the centalized decision make []. In this pape we study DoS attacks via pimay use emulation. In this type of attacks, a set of malicious seconday uses could foge the essential chaacteistics of the pimay signal tansmission to make othe good seconday uses believe that the pimay use is pesent when it is not. The seconday uses following nomal spectum evacuation pocess (the good uses) will vacate the spectum unnecessaily, esulting in what ae known as the pimay use emulation attacks (PUEA). PUEA become easie when enegy detection based mechanisms ae used fo identification of pimay activity, since the detecto only checks eceived enegy against a theshold athe than look fo paticula signal chaacteistics. Chen et al [] popose two mechanisms to detect PUEA: distance atio test distance diffeence test based on the coelation between the length of wieless link the eceived signal stength. They conside a single malicious use in a non-fading wieless envionment detect PUEA using the atio the diffeence, espectively, of the distances fom pimay tansmitte the malicious use, to the seconday uses equipped with global positioning system (GPS). In [], Chen et al discuss defense against PUEA by localization of the suspect tansmission via an undelying senso netwok compaing it with the known location of the pimay tansmitte. A mitigation technique fo DoS attacks aising fom faudulent epoting of sensing esults by malicious nodes is studied in []. The PUEA methods descibed thus fa do not take into account, the fading chaacteistics of the wieless envionment equie estimation of the location of the malicious uses via eithe a dedicated senso netwok o via significant enhancement of the seconday nodes themselves. The fist analytical expession fo the pobability of successful PUEA based on enegy detection was deived in [6], whee we modeled the eceived powe at a seconday use as a log-nomally distibuted om vaiable used Fenton s appoximation to detemine the mean the vaiance of this distibution. This was then used to detemine, a lowe bound on the pobability of successful PUEA using Makov inequality. In this pape, we popose a Neyman- Peason composite hypothesis test (NPCHT) a Wald s sequential pobability atio test (WSPRT) to detect PUEA in fading wieless envionments, without assuming additional featues to the seconday nodes o the pesence of dedicated senso nodes to assist in gatheing infomation about the diection of eceived signal. Fenton s appoximation is used to model the eceived powe at the seconday use fom the tansmission of the malicious uses. Simulations confim the theoetical esult that NPCHT allows the seconday use to keep the pobability of missing the pimay aound a desied theshold while tying to minimize the pobability of successful PUEA. Since the NPCHT cannot simultaneously povide a cap on the pobability of missing the pimay as well as the pobability of a successful PUEA, we develop the WSPRT, which will allow us this flexibility in etun fo some added time complexity, in tems of numbe of obsevations needed to aive at a decision. We show that with modest incease in computation, it is possible to mitigate PUEA significantly even when using only the enegy based detection. The est of the pape is oganized as follows. Section II pesents the system model the assumptions made to fomulate the poblem. The NPCHT as well as the WSPRT ae fomulated solved in Section III. In Section IV, we povide the simulation esults discussion. Section V pesents the conclusion. II. System Model In ou model all seconday malicious uses ae distibuted in a cicula gid of adius R as shown in Fig.. A pimay use is located at a distance of at least d p fom all othe uses. We conside enegy based mechanisms to detect the pesence of the pimay. Typical enegy based detection methods assume that the pimay is pesent if the eceived signal stength is -9dBm []. Such a sensing technique will cause seious secuity issues if malicious uses exist in the netwok. As descibed ealie, this detection method is susceptible to PUEA. In ode to mitigate this theat, we devise two hypothesis based testing mechanisms to decide if the pimay is tansmitting o if an attack is in pogess. The assumptions mathematical teminologies needed to deive the hypothesis tests ae listed below.. Thee is no communication o co-opeation between the seconday uses. The PUEA on each seconday use can be analyzed independent of each othe.. Thee ae M malicious uses in the system. M is a geometically distibuted om vaiable with the mean E[M] known to the seconday uses.. The pimay tansmitte is at a minimum distance of d p fom all the uses.. The positions of the seconday the malicious uses ae unifomly distibuted in the cicula

3 gid of adius R, thei positions ae statistically independent of each othe. 5. Fo the seconday use fixed at pola co-odinates (, θ ), no malicious uses ae pesent within a cicle of adius R centeed at (, θ ). We call R the exclusive distance fom the seconday use. Without this estiction, the powe eceived due to tansmission fom any subset of malicious uses pesent within this gid will be much lage than that due to a tansmission fom a pimay tansmitte thus esulting in failed PUEA all the time [6]. We use the pola coodinate system fo the est of the pape. 6. The co-odinates of the pimay tansmitte ae known to all the uses in the system. R R dp Good Seconday Use Malicious Seconday Use Pimay Tansmitte Figue : A typical cognitive adio netwok in a cicula gid of adius R consisting of good seconday uses malicious seconday uses. No malicious uses ae pesent within a adius R about each good seconday use. A pimay tansmitte is located at a distance of at least d p fom all othe uses. 7. The pimay tansmits at a powe P t the malicious at a powe P m. Malicious nodes do not use powe contol. 8. The RF signals fom the pimay tansmitte the malicious uses undego path loss lognomal shadowing. The Rayleigh fading is assumed to be aveaged out can hence be ignoed. This is because, the pobabilities scale linealy with the mean of the Rayleigh fading,, (as shown in [6]) = in most cases [8]. 9. The shadowing loss (expessed in db) at any seconday use both fom the pimay tansmitte fom any malicious use is nomally distibuted with mean vaiance σ p σ m, espectively.. We conside a fee space popagation model fo the signal fom the pimay tansmitte a III. two-ay gound model fo the signal fom the malicious uses thus esulting in a path loss exponent of fo the popagation fom the pimay tansmitte a path loss exponent of fo the popagation fom the malicious uses. This is because, the pimay tansmitte is so fa away fom the seconday malicious uses that the signal due to multi-path can be neglected. Howeve, the distances fom malicious uses ae not lage enough to ignoe the effects of multi-path [6]. Analytical Model Since thee is no co-opeation between the seconday uses, the pobability of PUEA on any use is the same as that on any othe use. Hence, without loss of geneality, we analyze the pobability density function (pdf) of the eceived signal at one seconday use. We tansfom the co-odinates of all malicious uses such that the seconday use of inteest lies at the oigin (i.e., at (, )). The tansfomed co-odinates of the pimay will then be (d p, θ p ). Note that the tansfomed co-odinates of the pimay will depend on the actual location of the seconday use of inteest will not be (d p, θ p ) fo all the seconday uses. Howeve, typically, d p >> R hence it is justified to appoximate the co-odinates of the pimay use to be (d p, θ p ) iespective of which seconday use we conside fo the analysis. The scenaio with the tansfomed co-odinates is shown in Fig.. By assumptions. 5. in Section II, all malicious nodes ae unifomly distibuted in the annula egion with adii R R. In ode to obtain a hypothesis test using NPCHT WSPRT, it is essential to obtain the pdf of the eceived signal at the seconday use due to tansmission by the pimay the malicious uses. We fist descibe the analysis to obtain the pdf in Section III.A. R R dp Good Seconday Use Malicious Seconday Use Pimay Tansmitte Figue : Scenaio with tansfomed co-odinates. The seconday use of inteest is at (,). Malicious uses ae unifomly distibuted in the annula egion (R, R). The pimay is at (d p, θ p ).

4 III.A. Pobability Density Function of the Received Signal Conside M malicious uses located at co-odinates ( j, θ j ) j M, whee M is a geometically distibuted om vaiable. The pobability mass function (pmf) of M, P {M = k} is theefoe given by P {M = k} = ( p) k p k =,,..., () whee p = E[M]. Fom assumptions. 5. in Section II, the position of the j th malicious use is unifomly distibuted in the annula egion between R R. Also, j θ j ae statistically independent j. The pdf of j, p( j ) is theefoe given by p( j ) = { j R R j [R, R] othewise, () while θ j is unifomly distibuted in ( π, π) j. The eceived powe at the seconday use fom the pimay tansmitte, P (p), is given by P (p) = P t d p G p, () whee G p = ξp ξ p N (, σp) as mentioned in Section II. Since P t d p ae fixed, the pdf of P (p), p (P ) (γ), follows a log-nomal distibution can be witten as p (P ) (γ) = whee A = Aσ p πγ exp ln ( log γ µ p) σ p, () µ p = log P t log d p. (5) The total eceived powe at the seconday node fom all M malicious uses is given by = M j= P m d j G j, (6) whee d j is the distance between the j th malicious use the seconday use G j is the shadowing between the j th malicious use the seconday use. As mentioned in Section II, G j = ξ j, whee ξ j N (, σm). Conditioned on the positions of all the malicious uses, each tem in the summation in the ight h side of Eqn. (6) is a log-nomally distibuted om vaiable of the fom ω j, whee ω j N (µ j, σm), whee µ j = log P m log d j. (7) As we had explained in [6], conditioned on the positions of all the malicious uses, P (m) can be appoximated as a log-nomally distibuted om vaiable whose mean vaiance can be obtained by using Fenton s method [9]. The pdf of P (m) conditioned on the positions of all M malicious uses, p (m) χ (χ ), can be witten as p (m) χ (χ ) = exp ( log χ ˆµ M ), (8) Aˆσ M πχ ˆσ M whee is the vecto with elements M ˆσ M ˆµ M ae given by ˆσ M = A ln ˆµ M = A ln p (m) (χ) = + (ea σm ) M j= eaµ j ( M j= eaµ j) (9) M j= e Aµ j A (ˆσ M σ m), () espectively. The pdf of the eceived powe fom all M malicious uses, p (m) (χ), can then be obtained by aveaging Eqn. (8) ove,, M can be witten as X "Z k= whee p( M) = [R,R] M p(m) χ (χ )p( M)d # P {M = k}, () M p( j ), p( j ) can be obtained j= fom Eqn. (). Evaluating Eqn. () is vey complex. Howeve, Eqn. () is an integal which can be looked upon as a weighted sum of conditional pdf s, each of which is log-nomal. Theefoe, applying Fenton s appoximation fo the weighted sum, the expession fo the pdf p (m) (χ) in Eqn. () can be appoximated as a lognomal distibution with paametes µ χ σ χ of the fom p (m) (χ) = Aσ χ πχ exp ( log χ µ χ) σ χ. () If P (m) is a log-nomally distibuted om vaiable with pdf given in Eqn. (), σχ µ χ can be The expessions in Eqns. (9) () can be obtained by following the steps specified in the Appendix in [6]. The expessions in Eqns. (8) () should also be conditioned aveaged ove the co-odinates ( hence have integations ove) θ, θ,, θ M. Howeve, fom Eqns. (7), (9) (), it is obseved that the expessions ae independent of θ, θ,, θ M. Theefoe, it is sufficient if the aveaging ( integations) ae pefomed ove,,, M.

5 obtained as in [] ( σχ = A ln V a µ χ = 6 A ln V a P (m) E [ ) + E [ P (m) i E hp (m) ] + E h ] () i 7 5. () Fom Eqn. (8),[ the expectation ] of P (m) conditioned on M, E P (m) M, the vaiance of ( ) P (m), V a P (m) M, can be obtained by aveaging [ ] ( ) E P (m) V a P (m) ove,,, M can be obtained in closed-fom as [ ] E P (m) M = MP m R e A σm, (5) R " V a M = MP m ea σ m R R 6R6 [ Theefoe, E ) can be calculated as [ E V a ] ] 6 R 6 R R ( V a! e A σ m R R #. (6) [ [ ]] = E E P (m) M, (7) h i i = E V a M + V a E h M. (8) Substituting the above expessions in Eqns. () (), we evaluate σ χ µ χ, which, in tun, can be substituted in Eqn. () to evaluate the pdf p (m) (χ). III.B. Neyman-Peason Composite Hypothesis Test to detect PUEA The Neyman-Peason composite hypothesis test can be used to distinguish between two hypotheses, given some constaints on the miss pobability. In ou case, the two hypotheses ae: H : H : Pimay tansmission in pogess Emulation attack in pogess. (9) The obsevation space is the sample space of eceived powe measued at the seconday use. It is obseved that thee ae two kinds of isks incued by a seconday use in this hypothesis test. False Alam: When the actual tansmission is made by malicious uses but the seconday decides that the tansmission is due to the pimay. In ou case, this is also the pobability of a successful PUEA. Miss: When the actual tansmission is made by the pimay tansmitte but the seconday decides that the tansmission is due to the malicious uses. This is a seious concen if the good seconday does not wish to violate the spectum etiquette. The Neyman-Peason citeion allows the seconday to minimize the pobability of successful PUEA while fixing the pobability of missing the pimay use at a desied theshold, α. The decision vaiable, Λ, is given by Λ = p(m) (x) p (P ) (x), () whee x is the measued powe of the eceived signal. In the above, p (P ) (x) p (m) (x) ae given by Eqns. () (), espectively. The decision is then made based on the following citeion: Λ λ Λ λ D : Pimay tansmission D : PUEA in pogess, () whee λ satisfies the constaint that miss pobability, P {D H }, is fixed at α, i.e., P {D H } = p (P ) (x)dx = α. () Λ λ The pobability of successful PUEA can be witten as P {D H } = p (m) (x)dx. () Λ λ We can also epesent the above detection statistic in shoth notation as Λ D D λ. () Let the eceived powe in db be denoted by y let a = σp σχ b = µ χ σχ µ p σp c = µ p σp µ χ σχ + ln σ p ln σ χ ln λ, (5)

6 by substituting p (P ) (x) p (m) (x), we obtain the decision statistic as: ay + by + c D D. (6) Without loss of geneality, we assume a. Let = b ac, two conditions ae of inteest: Case : a >, > The constaint of P {D H } = α can be witten as Φ b! aµ p + Φ b! + aµ p = α, (7) aσ p aσ p x whee Φ(x) = π P {D H } can be deived as P {D H } = Φ b +! aµ χ Φ aσ χ e t dt, b! aµ χ. (8) aσ χ Case : a <, > The constaint of P {D H } = α can be witten as Φ b! aµ p Φ aσ p P {D H } can be deived as P {D H } = Φ b +! aµ p = α, (9) aσ p b + aµ χ! b +! + aµ χ aσ χ + Φ. aσ χ () As is expected, the Neyman-Peason test only allows us to place a cap on one of the quantities: the miss pobability o the false alam pobability. In ou expeimental esults we found that unde cetain cicumstances, the pobability of false alam (successful PUEA) is vey high fo the desied pobability of miss. So, we now develop a Wald s sequential pobability atio test which allows the use to set thesholds fo both false alam miss pobabilities. This is possible, since Wald s test is set up to take moe than one sample obsevation if necessay to aive at a decision. III.C. Wald s Sequential Pobability Ratio Test to detect PUEA The WSPRT allows us to specify desied thesholds (α α espectively) fo both the false alam miss pobabilities. The decision vaiable afte n sequential tests, Λ n, is given by Λ n = n i= p (m) (x i ) p (P ) (x i ), () whee x i is the measued powe at the i th stage. In the above equation, p (P ) (x i ) p (m) (x i ) ae given by Eqns. () (), espectively. The decision is then made based on the following citeion: Λ n T = α α D : Pimay tansmission Λ n T = α α D : PUEA in pogess Othewise D : Take anothe obsevation. () The aveage numbe of obsevations equied to aive at a decision is given by [] E[n H k ] = { ( α ) ln T +α ln T E[f(x ) H ] k = α ln T +( α ) ln T E[f(x ) H ] k =, () whee the function f(x ) = ln Λ. Fom Eqns. (), () (), we can deive the expession fo E[f(x ) H ] E[f(x ) H ] as follows: E[f(x ) H ] = ln + µ p(σ pµ χ σ χµ p ) σ pσ χ E[f(x ) H ] = ln + µ χ(σ pµ χ σ χµ p ) σ pσ χ ( σp σ χ ) + σ χµ p σ pµ χ σ pσ χ + (σ χ σp)(σ p + µ p) σpσ χ, () ( σp σ χ ) + σ χµ p σ pµ χ σ pσ χ + σ χ σp σpσ χ (σχ + µ χ). (5) Substituting E [f(x ) H ] E [f(x ) H ] in Eqn. (), we evaluate E [n H ] E [n H ]. Based on Section IV.B, we can see that though expeimental esults of WSPRT do not pefectly match thei theoetically designed citeion, they can significantly lowe the pobability of successful PUEA than NPCHT do. The pice WSPRT pays to achieve a fine decision is to take moe obsevations. IV. Simulations We conside the following values of the system paametes fo ou numeical simulations. The vaiances fo the pimay malicious tansmissions ae assumed to be σ p = 8 σ m = 5.5, since we can model the pimay malicious tansmissions as those occuing in uban sububan envionments [8]. A pimay tansmitte (a TV towe), located at a distance of d p = km to the seconday use, has a tansmit powe of P t = kw. The tansmit powe of the malicious uses, P m, is taken to be Watts as in []. The exclusive distance fom the seconday use, R, is fixed at m, the same as in [6]. The netwok adius, R, is inceased fom m to 7m, thus changing the aveage distance fom the malicious

7 use to the seconday use accodingly. The numbe of malicious uses is assumed to be a geometically distibuted om vaiable with E[M] equal to 5. As the calculation shows, the above numeical paametes always guaantee the case whee a < δ > in the NPCHT. In ou simulations, we assume that the pimay use is modeled as a Benoulli ( ), which means that thee is equal pobability of the pimay to be ON o OFF. The pimay use tansmissions ae simulated as pe the distibution discussed ealie which includes path loss shadowing. To simulate the PUEA, we fist geneate a geometically distibuted om numbe, M, epesenting the numbe of malicious uses. We then geneate M independent identically distibuted (i.i.d.) sets of co-odinates fo M malicious uses, such that the malicious uses ae unifomly distibuted in the annulus with adii R R. The eceived powe fom the tansmission of all M malicious uses is calculated based on Eqn. (6), including path loss i.i.d. shadowing. Fo each value of R, we un, simulations. We calculate false alam pobabilities miss pobabilities by counting the numbe of times that the decision statistic meets the coesponding decision citeion. Fo WSPRT, we also ecod aveage numbe of obsevations equied to make a decision in each simulation. IV.A. Neyman-Peason Composite Hypothesis Test Results The esults of NPCHT with theoetical pobability of missing the pimay use set to α=. ae shown in Fig.. It is obseved fom Fig. (a) that the pobability of false alam ises then falls down with inceasing value of R. This is because, fo a given R, if R is small, i.e., malicious uses ae close to the seconday use, the total eceived powe fom all malicious uses is likely to be lage than that eceived fom the pimay tansmitte, thus deceasing the pobability of successful PUEA. Similaly, fo lage R, the total eceived powe fom the malicious uses may not be enough to successfully launch a PUEA. Fig. (b) shows that the expeimental pobability of missing the pimay use is always close to the equied value (within ±. of the desied value). As we lowe α fom. to., the maximum eo between the expeimental cuve the theoetical one falls fom., shown in Fig. (a), to.8, shown in Fig. (a). These discepancies exist, because we needed to make appoximations while deiving the expessions fo the eceived powe. Howeve, since the expeimental theoetical values ae not fa apat, ou appoximations ae faily good. Fom Fig. (a) Fig. (a) we note that as α is deceased, the pobability of successful PUEA inceases. This is expected, since NPCHT only allows a theshold to be set on one of these paametes. IV.B. Wald s Sequential Pobability Ratio Test Results Fig. 5 shows the esults of WSPRT with thesholds fo the pobability of successful PUEA, pobability of missing pimay use set to. each. Although the expeimental cuve in Fig. 5(a) goes above the theoetical one, we achieve much lowe pobabilities of successful PUEA compaed to Fig. (a). In fact, the maximum pobability of successful PUEA in the NP test can go as high as.778 wheeas in the Wald s test we can limit this to.7. The lowe pobabilities of successful PUEA ae achieved at the cost of moe obsevations as shown in Fig. 5(c) Fig. 5(d). It is obseved that numbe of obsevation behaves simila to the pobability cuves. This is because, moe obsevations ae always taken if a decision can not be made easily, whee decision eo pobabilities also tend to be elatively high. Note that the gap between the expeimental theoetical cuves is typical of WSPRT because, the expession fo the expected numbe of obsevations in Eqn. () is an appoximation athe than an exact expession []. Fig. 6(a) shows the esults obtained when the theshold fo PUEA is set to.. Compaing this with Fig. 5(a) we see that fo any α, it is not possible to achieve abitay lowe pobabilities of successful PUEA. Note, howeve, that it is always possible to make sue that the pobability of missing pimay use stays stictly below the equied theshold, which can be seen fom Fig. 5(b), Fig. 6(b) Fig. 7(b). This is paticulaly impotant in CRN to ensue that the secondaies still obey the spectum shaing etiquette. As both α α ae loweed to. (Fig. 7), only the expeimental cuve of miss pobability in Fig. 7(b) deceases accodingly. This indicates that it is not possible to always keep both the false alam pobability as well as the miss pobability below abitaily desied thesholds. Fom the cuves showing the numbe of obsevations equied to make a decision (Fig. 5(c), Fig. 6(c) Fig. 7(c)), it can be noticed that moe obsevations ae equied as the α α ae deceased. This is because, fom Eqn. (), as α α deceases, the theshold T deceases the theshold T inceases which effectively educes the ange of values of the

8 test statistic fo which a decision is taken. Thus, it is moe likely that the seconday use takes decision D (i.e., obseves moe samples). Theefoe, thee is a tadeoff between eliable decision time to detect. V. Conclusion We poposed a Neyman-Peason composite hypothesis test (NPCHT) a Wald s sequential pobability atio test (WSPRT) to detect pimay use emulation attacks (PUEA) in cognitive adio netwoks. Both WSPRT NPCHT esulted in a ange of adii in which PUEA wee most successful. Fo a desied theshold on the pobability of missing the pimay, WSPRT was found to achieve 5% eduction in the pobability of successful PUEA compaed to NPCHT. We ae cuently investigating the extension of ou analysis fo othe distibutions of the numbe of malicious uses, M, detemination of the best fit fo the distibution of M. The extension of ou analysis to include powe contol at the malicious uses is a topic fo futhe investigation. Refeences False Alam Pobability (P{D H (a) Pobability of successful PUEA using the NPCHT. The aveage numbe of malicious uses is fixed at 5. [] J. Mitola G. Maguie, Cognitive adio: Making softwae adios moe pesonal, IEEE Pesonal Commun., vol. 6, no., pp. 8, Aug [] S. Haykin, Cognitive adio: Bain empoweed wieless communications, IEEE Jl. on Sel. Aeas in Commun., vol., no., pp., Feb. 5. Miss Pobability (P{D H [] I. F. Akyildiz, W. Lee, M. C. Vuan, S. Mohanty, NeXt geneation/dynamic spectum access/cognitive adio wieless netwoks: A suvey, Elsevie Jl. on Compute Netwoks, vol. 5, no., pp. 7 59, Sep. 6. [] IEEE Stads fo infomation technology- Telecommunications infomation exchange between systems- Wieless Regional Aea Netwoks-Specific Requiements- Pat - Cognitive wieless RAN medium access contol (MAC) physical laye (PHY) specifications: Policies pocedues fo opeation in the TV bs, Jun (b) Pobability of missing pimay use using the NPCHT. Note that the expeimental values ae not too fa fom the desied theshold. Figue : NPCHT with theoetical pobability of missing pimay use α=.. [5] [Online]. Available: [6] C. Codeio, K. Challapali, D. Biu, S. Shanka, IEEE 8.: The fist wold-

9 .9.5. False Alam Pobability (P{D H Miss Pobability (P{D H (a) Pobability of successful PUEA using the NPCHT. The aveage numbe of malicious uses is fixed at 5. (b) Pobability of missing pimay use using the NPCHT. Note that the expeimental values ae not too fa fom the desied theshold. Figue : NPCHT with theoetical pobability of missing pimay use α= Toleated False Alam Pobability (P{D H Miss Pobability (P{D H (a) Pobability of successful PUEA (b) Pobability of missing pimay use Numbe of Obsevations (E[n H ]) 5 Numbe of Obsevations (E[n H ]) (c) Aveage numbe of obsevations when malicious uses ae tansmitting (d) Aveage numbe of obsevations when pimay use is tansmitting Figue 5: WSPRT with theoetical pobability of successful PUEA α missing pimay use α =.. =. theoetical pobability of

10 Toleated False Alam Pobability (P{D H Miss Pobability (P{D H (a) Pobability of successful PUEA (b) Pobability of missing pimay use Numbe of Obsevations (E[n H ]) 6 5 Numbe of Obsevations (E[n H ]) (c) Aveage numbe of obsevations when malicious uses ae tansmitting (d) Aveage numbe of obsevations when pimay use is tansmitting Figue 6: WSPRT with theoetical pobability of successful PUEA α missing pimay use α =.. =. theoetical pobability of

11 Toleated False Alam Pobability (P{D H Miss Pobability (P{D H (a) Pobability of successful PUEA (b) Pobability of missing pimay use Numbe of Obsevations (E[n H ]) Numbe of Obsevations (E[n H ]) (c) Aveage numbe of obsevations when malicious uses ae tansmitting (d) Aveage numbe of obsevations when pimay use is tansmitting Figue 7: WSPRT with theoetical pobability of successful PUEA α missing pimay use α =.. =. theoetical pobability of

12 wide wieless stad based on cognitive adios, Poc., IEEE Symposium of New Fonties in Dynamic Spectum Access Netwoks (DyS- PAN 5), pp. 8 7, Nov. 5. [7] E. Visotsky, S. Kuffne, R. Peteson, On collaboative detection of TV tansmission in suppot of dynamic spectum shaing, Poc., IEEE Symposium of New Fonties in Dynamic Spectum Access Netwoks (DySPAN 5), pp. 8 5, Nov. 5. [8] X. Liu Z. Ding, ESCAPE: A channel evacuation potocol fo spectum-agile netwoks, Poc., IEEE Symposium of New Fonties in Dynamic Spectum Access Netwoks (DySPAN 7), pp. 9, Ap. 7. [9] G. Jakimoski K. Subbalakshmi, Denialof-sevice attacks on dynamic spectum access netwoks, IEEE CogNets Wokshop, IEEE Intl. Conf. on Commun. (ICC 8), pp. 5 58, May 8. [] G. Jakimoski K. P. Subbalakshmi, Towads secue spectum decision, To appea, IEEE Intl. Conf. on Commun. (ICC 9), Jun. 9. [] R. Chen J. M. Pak, Ensuing tustwothy spectum sensing in cognitive adio netwoks, Poc., IEEE Wokshop on Netwoking Technol. fo Softwae Defined Radio Netwoks (SDR 6), pp. 9, Sep. 6. Fonties in Dynamic Spectum Access Netwoks (DySPAN 8), Oct. 8. [6] S. An, Z. Jin, K. P. Subbalakshmi, An analytical model fo pimay use emulation attacks in cognitive adio netwoks, Poc., IEEE Symposium of New Fonties in Dynamic Spectum Access Netwoks (DySPAN 8), Oct. 8. [7] Z. Jin, S. An, K. P. Subbalakshmi, Detecting pimay use emulation attacks in dynamic spectum access netwoks, To Appea, IEEE Intl. Conf. on Commun. (ICC 9), Jun. 9. [8] T. S. Rappapot, Wieless Communications: Pinciples Pactice. Pentice Hall Inc., New Jesey, 996. [9] L. F. Fenton, The sum of log-nomal pobability distibutions in scatte tansmission systems, IRE Tans. on Commun. Systems, vol. 8, no., pp , Ma. 96. [] S. Ross, Pobability Models. Academic Pess,. [] J. L. Melsa D. L. Cohn, Decision Estimation Theoy. McGaw-Hill Inc., 978. [] R. Chen, J. M. Pak, J. H. Reed, Defense against pimay use emulation attacks in cognitive adio netwoks, IEEE Jl. on Sel. Aeas in Commun.: Spl. Issue on Cognitive Radio Theoy Applications, vol. 6, no., pp. 5 7, Jan. 8. [] R. Chen, J. M. Pak, K. Bian, Robust distibuted spectum sensing in cognitive adio netwoks, Poc., IEEE Conf. on Comp. Commun. (INFOCOM 8), pp , Ap. 8. [] T. C. Clancy N. Goegen, Secuity in cognitive adio netwoks: Theats mitigation, Poc., Intl. Conf. on Cognitive Radio Oiented Wieless Netwoks Commum. (Cown- Com 8), May 8. [5] A. Sethi T. X. Bown, Hamme model theat assessment of cognitive adio denial of sevice attacks, Poc., IEEE Symposium of New