Coordinated Denial-of-Service Attacks in IEEE Networks

Transcription

1 Coordnated Denal-of-Servce Attacks n IEEE Networks Y Tan Department of ECE Stevens Insttute of Technology Hoboken, NJ Emal: ytan@stevens.edu Shamk Sengupta Department of Math. & Comp. Sc. John Jay College of Crmnal Justce CUNY, New York, NY Emal: ssengupta@jjay.cuny.edu K.P. Subbalakshm Department of ECE Stevens Insttute of Technology Hoboken, NJ Emal: ksubbala@stevens.edu Abstract The cogntve rado enabled IEEE wreless regonal area network (WRAN s desgned to opportunstcally utlze the unused or under-utlzed TV bands. However, due to the lack of proactve securty protocols and proper nteracton polces among the secondary networks themselves, the IEEE networks are vulnerable to varous denal-of-servce (DoS threats. In ths paper, we study the mpact of coordnated DoS attacks on IEEE networks from the malcous nodes perspectve. Assumng that multple malcous nodes wll launch coordnated attacks, we formulate a cooperatve game among the malcous nodes. The expresson of the net payoff s derved and the optmal decson strategy for the malcous nodes s obtaned numercally. Smulaton results demonstrate that the coordnated attack approach can enhance as hgh as -5% more net payoff for the malcous nodes than the uncoordnated attack. I. INTRODUCTION The conventonal fxed spectrum assgnment polcy has resulted n suboptmal use of spectrum resource leadng to over-utlzaton n some bands and under-utlzaton n others []. Ths observaton has led to the recent spectrum polcy reforms by Federal Communcaton Commsson (FCC. Ths goal, of dynamc spectrum access (DSA, s expected to be acheved va the recently proposed concept of cogntve rados. The IEEE s an emergng standard for cogntve rado-based wreless regonal area networks (WRANs. The IEEE standard ams at usng DSA to allow the geographcally unused, lcensed TV frequency spectrum to be used by unlcensed users on a non-nterferng bass [2]. To protect the prmary ncumbent servces, IEEE devces are requred to perform perodc spectrum sensng and evacuate promptly upon the return of the lcensed users. Even though the prmary user protecton mechansms have been proactvely specfed, nether the secondary-secondary nteracton mechansms nor the protecton of secondary devces/networks have been specfcally defned or addressed n IEEE standard [3]. Hence, the IEEE networks are vulnerable to denal-of-servce (DoS attacks, by whch the attacker wll prevent the secondary networks from usng the spectrum band effectvely or at all. Several research works are nvestgatng nto the dfferent securty aspects n cogntve rado networks [4], [5]. However, most of these works ether deal wth sngle malcous node or uncoordnated attacks by multple malcous nodes or are not specfc to IEEE In ths paper, we address a key fundamental queston: what f multple malcous nodes launch DoS attacks n a coordnated manner? Recently, a hacker brought down the Twtter by usng thousands of malware-nfected personal computers to launch DoS attacks coordnately, whch made mllons of Twtter users unable to access the servce. In the wreless DSA networks, ths knd of threat s even worse as specfc securty polces have not yet been developed. Thus, understandng ths attack model s absolutely crtcal. In ths work, we study the coordnated attack usng the concept of cooperatve game theory. In our model, the common goal of the malcous nodes s to dsrupt the communcatons of protocol complant IEEE secondary networks. We assume that the malcous nodes are also spectrum agle, but do not have a pror knowledge of the spectrum occupancy at any gven tme. We model ths problem as a cooperatve game where the malcous nodes wll collaborate to attack as many secondary networks as possble whle keepng ther costs to a mnmum. As a collaboratve team, the malcous nodes try to maxmze the net payoff rather than ther ndvdual payoffs. We derve the theoretcal expresson of the net payoff as well as the optmal strategy for the malcous nodes. Smulaton results demonstrate that the cooperaton among malcous nodes can remarkably ncrease ther net payoff compared to the noncooperatve attack. To the best of our knowledge, ths work s the frst attempt to analyze and understand the coordnated DoS attack n IEEE networks. The rest of ths paper s organzed as follows. The system model s dscussed n Secton II. In Secton III, we derve the expresson of the net payoff and the optmal strategy for malcous nodes. Secton IV presents the smulaton results and conclusons are drawn n the last Secton. II. SYSTEM MODEL A typcal IEEE cell s a sngle-hop, pont-tomultpont wreless network, n whch a central Base Staton (BS controls the medum access of a number of assocated consumer premse equpments (CPEs. In our model, we consder N avalable spectrum bands not beng used by prmary ncumbents, and n (n N such IEEE secondary networks. For smplcty, we assume that each secondary network transmts n a spectrum band free of nterference

2 of other secondary networks to guarantee hgh qualty-ofservce (QoS. Ths can be acheved va the IEEE selfcoexstence mechansm as presented n [6]. Thus, n out of N spectrum bands are concurrently used by the secondary networks. We refer to these n spectrum bands as busy bands and other N n spectrum bands as vacant bands. Moreover, let there be m (m N malcous nodes amng to attack the secondary networks. They can swtch among N bands but do not have a pror knowledge about whch bands the secondary networks are usng at any gven tme. We assume that the malcous nodes can use the common control channel (CCC to coordnate ther actons [7]. Before we begn the analyss, we frst defne the notatons that wll be used throughout the paper: The sum of payoffs for all malcous nodes. Indvdual payoff The payoff for one malcous node. c Swtchng cost: the energy consumed n swtchng from one spectrum band to another. g Attack gan: the ncentve obtaned by successfully attackng one secondary network. It s the motvaton for the malcous nodes to launch DoS attacks. A. Cooperatve Game Formulaton In the tradtonal non-cooperatve game, all players are assumed selfsh and act n a dstrbuted manner,.e., they make decsons ndependently to maxmze ther ndvdual payoffs. The soluton to the non-cooperatve game s the Nash equlbrum, whch s defned as a strategy set such that no player can ncrease ts ndvdual payoff by changng the strategy unlaterally [8]. However, the non-cooperatve Nash equlbrum just emphaszes the equlbrum among the ndependent players rather than ther common nterests [8]. If all players have the same objectve (e.g., n our case, all malcous nodes am to dsrupt the communcatons of IEEE secondary networks, non-cooperatve Nash equlbrum mght not be the best soluton because t does not take nto account the cooperaton among players. Hence, we study the behavors of malcous nodes from the cooperatve game theoretc pont of vew and nvestgate whether the cooperaton could mprove the benefts for the malcous nodes. Based on the system model, we consder m malcous nodes as the game players. Rather than beng "always greedy and proft seekng", all players are selfless and work as a collaboratve team. Each malcous node has two possble choces: stayng n the current band (savng swtchng cost or swtchng to other bands (expectng to attack another secondary network. If the malcous nodes successfully attack a secondary network, they wll obtan the attack gan, g. On the other hand, every swtch wll ncur a swtchng cost, c. The man assumpton n a cooperatve game s that all players wll reach a grand coalton before the game starts and are not allowed to devate from ths coalton. Otherwse, the players wll act ndvdually n a non-cooperatve way. The challenge n reachng an agreement s to allocate the total utltes to the players farly and effectvely. In our case, we apply Nash Barganng soluton whch provdes farness, unqueness and Pareto-optmzaton [8]. Due to the homogenety of all players, the most effectve way to dvde the utlty s equal allocaton. Thus, the optmzaton problem for the cooperatve game s to fnd a mechansm of swtchng or stayng for the malcous nodes such that the net payoff can be maxmzed. III. ANALYSIS OF NET PAYOFF AND OPTIMAL STRATEGY The pure strateges for the malcous nodes, n our case, are to ether stay n the current band or to swtch to another band. However, f the malcous nodes choose to stay n the same band always, they wll mss opportuntes to attack other secondary networks. On the other hand, f the strategy s to always swtch, ths could lead to some unnecessary costs. Therefore, t s necessary for the malcous nodes to adopt a mxed strategy space to fnd the optmal soluton. Assumng all players make ther moves smultaneously, we defne the mxed-strategy space for the malcous nodes as: S mxed = {(Swtch prob. = p, (Stay prob. = p}. ( That s, the players wll swtch wth probablty p and stay wth probablty p. The net payoff for the malcous nodes s equal to the total attack gan, whch depends on the number of secondary networks beng successfully attacked, mnus total swtchng costs. That n turn depends on how many malcous nodes actually choose to swtch. We consder two cases n ths game: Specal case: The game starts wth all the malcous nodes coexstng n one busy spectrum band. General case: The game starts wth the malcous nodes scattered over the spectrums bands. A. Specal Case In order to maxmze the net payoff, one malcous node wll be selected by the central entty to make sure the secondary network n the current busy band can be successfully attacked, and other m can choose to ether stay or swtch. The malcous nodes stayng n ths spectrum band wll jontly launch DoS attack n ths busy band, whereas the malcous nodes that swtch wll try to attack more secondary networks n other spectrum bands. As a result, the probablty that out of m malcous nodes wll swtch, Q(, follows a bnomal dstrbuton as: ( m Q( = p ( p m, m. (2 Moreover, snce the players have no dea about whch bands are occuped by the secondary networks, some malcous nodes may swtch to vacant bands. Let q be the probablty that the malcous node swtches to a busy band, whch s q = n N. Hence, the probablty that k out of swtchng malcous nodes land n the busy bands, R(k, s calculated as: ( R(k = q k ( q k, k. (3 k

3 Note that, among these k malcous nodes who swtch to busy bands, some may stll land up n the same band. Hence, t s necessary to know the number of busy bands that the malcous nodes have landed n. Thus, the probablty that j out of n secondary networks have been successfully attacked by k malcous nodes, f(j, s gven by (see detals n Appendx-I: f(j = ( n ( k j j ( k+n 2 n 2. (4 Let j be the random varable representng the number of compromsed secondary networks, then, the expected value of j, E(j, s gven by: { k E(j = j= f(j j, k > (5, k = Consoldatng Equatons (2 (5, we derve the expected net payoff, U(p, for the malcous nodes as: ( m ( m U(p = g Q( R(k E(j + c Q(. = k= = (6 The frst term on the rght hand sde (RHS of the equaton represents the expected attack gan and the second term represents the expected swtchng cost for the whole team. Based on the equal allocaton prncple, the common goal for the malcous nodes s to maxmze the net payoff. Thus, the optmal swtchng probablty p s calculated as: B. Generalzed Case p = arg max U(p. (7 p [,] In the generalzed case, the malcous nodes are randomly scattered over the avalable spectrum bands. The central entty plays an mportant role n the decson makng process of the players: Every malcous node senses ts spectrum band (to see whether t s used by a secondary network or not and reports back to the central entty before takng actons. The central entty sends the consoldated pcture back to the malcous nodes. To maxmze the attack gan, the malcous nodes, f they choose to swtch, wll potentally explore other unknown spectrum bands. Based on above assumpton, the malcous nodes can be dvded nto two subgroups: those that stand n the vacant bands and those that are n the busy bands. Those n the vacant bands wll defntely swtch to other spectrum bands because there s no ncentve n contnung to stay n a vacant band. On the other hand, those n the busy bands wll follow a smlar procedure to the specal case,.e., only one malcous node wll be selected to stay n each band and others can freely choose to ether stay or swtch wth a certan probablty. Let us suppose that, n a gven tme slot, the malcous nodes are scattered n L out of N bands, n whch h bands are used by h secondary networks. Thus, h malcous nodes wll be selected to stay n these busy bands. Let r be the random varable representng the number of malcous nodes landng n vacant bands. Therefore, the malcous node who chooses to swtch wll try to reach one of the other N L bands whose status s unknown. Thus, the mxed strategy space n Equaton ( s only appled to m h r malcous nodes. Denotng p as the swtchng probablty for the malcous nodes n the generalzed case and usng the same logc as n the specal case, we have followng expressons: Snce there are h players who defntely stay and r players who defntely swtch, we just consder the rest m h r players. The probablty of out of m h r malcous nodes choosng Swtch, Q (, s calculated as: ( m h r Q ( = p ( p m h r, m h r. (8 Snce swtchng malcous nodes wll explore N L spectrum bands whose status s unknown, n whch n h bands are used by secondary networks, the probablty for them to swtch to the busy bands, q, s q = n h N L. Together wth other r swtchng players, the probablty of k out of + r malcous nodes landng n the busy bands, R (k, s calculated as: ( + r R (k = k q k ( q +r k, k + r. (9 The probablty that j out of n h secondary networks have been successfully attacked s gven by f (j = ( n h ( k j j ( k+n h n h. ( The expected value for j, E (j, s calculated as: { k E (j = j= f (j j, k >, k = ( Consoldatng Equatons (8 (, we derve the net payoff for the malcous nodes n the generalzed case, U (p, as: ( m h r +r U (p = g Q ( R (k E (j + h c = k= ( m h r = Q ( + r. (2 Smlarly, the optmal strategy for the malcous nodes n the generalzed case s gven by: C. Numercal Results p = arg max p [,] U (p (3 Both Equaton (7 and Equaton (3 can be solved numercally. We set the parameter values as g = 5 and c = 2. For example, wth network parameters as: N = 5, n = 3 and m = 2, the numercal results for the specal and generalzed cases are shown n Fg.. As llustrated n Fg., there exsts a maxmum net payoff for the malcous nodes n each case, correspondng to a unque optmal strategy,.e., p = and p = 3 for the specal and generalzed case respectvely.

4 (a p p Fg.. The net payoff for the malcous nodes wth respect to swtchng probablty. (a specal case; (b generalzed case (wth temporary state as: L =, h = 6 and r = 6. IV. SIMULATION RESULTS AND INTERPRETATION In ths secton, we conduct smulatons to evaluate mprovement acheved by the cooperaton among the malcous nodes. We consder N = 5 avalable spectrum bands and also set g = 5 and c = 2. The smulaton results are averaged over, Monte Carlo smulatons. A. Smulatons for the Specal Case We frst conduct the smulaton for the specal case. Fg. 2 shows the theoretcal and smulaton results for the optmal swtchng probablty for the malcous nodes, p, for m malcous nodes. As evdent, the smulaton results matches the theoretcal results closely. Another observaton s that the probablty of swtchng s gradually convergng to wth the ncrease n the number of secondary networks, n. Ths s because, more secondary networks exstng around the spectrum bands mples better chance for the malcous nodes to swtch to the busy bands. Note that the theoretcal results are numercally derved from Equaton (7. (b we fx the number of the malcous nodes as m = 2, and vary the number of the secondary networks. As llustrated n ths fgure, the net payoff obtaned by coordnated attack acheves approxmate 5% mprovement to the non-cooperatve attack. Note that the strategy for the non-cooperatve game s Nash equlbrum strategy, whch, n our case, t s the swtchng probablty for each malcous node (see detals n Appendx-II. Wth the ncrease n the number of secondary networks, the malcous nodes followng the optmal strateges can get greater net payoff as expected Non cooperatve Cooperatve Fg. 3. The comparson of net payoff between the cooperatve and noncooperatve game for the specal case Non cooperatve (Case Non cooperatve (Case 2 Non cooperatve (Case 3 Cooperatve m= (a Theoretcal results Smulaton results m=25 Theoretcal results Smulaton results (c m= (b Theoretcal results Smulaton results m=3 Theoretcal results Smulaton results (d Fg. 2. The optmal probablty of swtchng for the malcous nodes, p, for the specal case wth varyng number of malcous nodes, m, and secondary networks, n. The comparson of the net payoffs between the cooperatve game and non-cooperatve game s shown n Fg. 3, n whch Fg. 4. The comparson of net payoff between the cooperatve and noncooperatve game for the generalzed case. B. Smulatons for the Generalzed Case In the general case, we consder m = 2 malcous nodes wth temporary state as: L =, h = 6, r = 6. When comparng performances of the cooperatve and noncooperatve attacks n the general case, we need to make malcous nodes who have optons to stay or swtch have the same Nash equlbrum pont n the non-cooperatve game such that t s calculable. Hence, we consder three partcular cases for malcous nodes as follows: Case : 4 out of 6 busy bands have multple players (each band wth 2 players and the other 2 busy bands have only one player. Case 2: 2 out of 6 busy bands have multple players (each band wth 5 players and the other 4 busy bands have only one player. Case 3: out of 6 busy bands have multple players (9 players n ths band and the other 5 busy bands have only one player.

5 In each case mentoned above, the malcous nodes n the busy bands are equvalent and thus have the same Nash equlbrum strategy, whch can be calculated followng the same logc gven n Appendx II. Fg. 4 shows the smulaton results of the comparson of net payoffs between the cooperatve and non-cooperatve attacks wth varyng number of secondary networks. Smlar to the general case, the cooperatve attack n the generalzed case also clearly outperforms the non-cooperatve attack from the malcous nodes perspectve. V. CONCLUSION In ths paper, we nvestgated the mpact of coordnated DoS attacks on IEEE networks from the perspectve of malcous nodes. Usng the concept of cooperatve game theory, we modeled the malcous nodes as a collaboratve team amng to maxmze ther net payoff by dsruptng the communcatons of good secondary networks. We analytcally derved the theoretcal expresson of net payoff and numercally obtaned the optmal strateges for the malcous nodes group from two dfferent perspectves. Smulaton results demonstrated that by takng the coordnated approach, the malcous nodes can acheve as hgh as -5% more net payoff than that f they do not cooperate. ACKNOWLEDGEMENT Ths work s partally supported by NSF Trustworthy Computng wth grant number 978 and NIJ NJ-IJ. APPENDIX I DERIVATION OF PROBABILITY f(j f(j s the probablty that j out n secondary networks are successfully attacked by k malcous nodes who swtch to these n busy bands and s gven by f(j = X Y Z, where X: Number of ways n whch j out of n secondary networks can be selected, whch s ( n j. Y : Number of ways n whch a group of k malcous nodes can brng down exactly j secondary networks. Ths s equvalent to the number of dstnct postve ntegervalued vector (x, x 2,, x j satsfyng x + x x j = k, whch s ( k j [9]. Z: Number of ways n whch k malcous nodes can dstrbute n n busy bands. Ths s equvalent to the number of dstnct nonnegatve nteger-valued vectors (x, x 2,, x n satsfyng x + x x n = k, whch s ( k+n 2 n 2 [9]. Therefore, f(j s gven by: f(j = ( n ( k j j ( k+n 2 n 2. (4 APPENDIX II THE MIXED-STRATEGY NASH EQUILIBRIUM OF THE NON-COOPERATIVE GAME FOR THE SPECIAL CASE In the non-cooperatve game, each malcous node s selfsh and can choose to swtch or stay ndependently. We assume that f more than one malcous nodes jontly attack the same secondary network n a spectrum band, each of them can get the average attack gan. For example, f 3 malcous nodes land n the same busy band, each can obtan g/3 attack gan. Wthout loss of generalty, we consder one typcal player, s, and the same reasonng apples to all other players. ( Expected payoff for the player s to stay: Let us denote θ as the swtchng probablty and so the probablty that out of other m malcous nodes wll also stay, Q stay (, s calculated as: ( m Q stay ( = ( θ θ m, m. (5 Thus, the expected payoff for player s to stay s gven by: E(stay = m = Q stay ( g +. (6 ( Expected payoff for the player s to swtch: The probablty that out of other m malcous nodes wll also swtch wth player s, Q swtch, s calculated as: ( m Q swtch ( = θ ( θ m, m. (7 Note that the probablty for the player s to swtch to the busy bands s also q = n N. Moreover, the probablty that exactly j out of players swtch to the same band wth player s, H(j, s calculated as: ( H(j = j (N j (N 2 N j. (8 Thus, the expected payoff for player s to swtch s gven by: E(swtch = m = j= q Q swtch ( H(j g c. (9 j + Consoldatng ( and (, the mxed-strategy Nash equlbrum for the malcous nodes, θ, s obtaned by mposng E(stay = E(swtch, whch can be solved numercally. REFERENCES [] F. C. Commsson, Spectrum polcy task force report, IEEE Transactons on Informaton Forenscs and Securty, pp. 2 55, Nov 22. [2] C. R. Stevenson, G. Chounard, Z. Le, W. Hu, S. J. Shellhammer, and W. Caldwell, IEEE 82.22: The frst cogntve rado wreless regonal area network standard, IEEE Communcaton Magazne, Jan. 29. [3] K. Ban and J.-M. J. Park, Securty vulnerabltes n IEEE 82.22, WICON 8, pp. 9, 28. [4] T. Clancy and N. Goergen, Securty n cogntve rado networks: Threats and Mtgaton, CrownCom 28, pp. 8, May 28. [5] R. Chen, J.-M. Park, and J. Reed, Defense aganst prmary user emulaton attacks n cogntve rado networks, Selected Areas n Communcatons, IEEE Journal on, vol. 26, no., pp , Jan. 28. [6] S. Sengupta, R. Chandramoul, S. Brahma, and M. Chatterjee, A game theoretc framework for dstrbuted self-coexstence among IEEE networks, IEEE GLOBECOM, Dec. 28. [7] I. F. Akyldz, W.-Y. Lee, M. C. Vuran, and S. Mohanty, Next generaton/dynamc spectrum access/cogntve rado wreless networks: a survey, Comput. Netw., vol. 5, no. 3, pp , 26. [8] R. Myerson, Game Theory: Analyss of Conflct. Harvard Unv., 997. [9] S. Ross, A Frst Course n Probablty. Prentce Hall, 7 edton, 25.