Trust-based Service Management of Mobile Devices in Ad Hoc Networks

Transcription

1 Trus-based Service Managemen of Mobile Devices in Ad Hoc Neworks Yaing Wang, Ing-Ray Chen Virginia Tech Deparmen of Compuer Science Falls Church, VA, USA {yaingw, Jin-Hee Cho U.S. Army Research Laboraory Compuaional and Informaion Sciences Direcorae Adelphi, MD, USA Absrac Wih he proliferaion of fairly powerful mobile devices and ubiquious wireless echnology, radiional mobile ad hoc neworks (MANETs) now migrae ino a new era of service oriened MANETs wherein a mobile device can provide and receive service from oher mobile devices i encouners and ineracs wih. We discuss our ongoing research effors in rus managemen and rus-based algorihm design for service-oriened MANET applicaions o answer he challenges of MANET environmens, including no cenralized auhoriy, dynamically changing opology, limied bandwidh and baery power, limied observaions, unreliable communicaion, and he presence of malicious nodes who ac o break he sysem funcionaliy as well as selfish nodes who ac o maximize heir own gain. We also highligh key ideas and experiences learned, and provide fuure research direcions. Keywords-service-oriened mobile ad hoc neworks; muliobjecive opimizaion; rus; performance analysis. I. INTRODUCTION An auonomous service-oriened mobile ad hoc nework (MANET) is populaed wih service providers (SPs) and service requesers (SRs). A realizaion of service-oriened MANETs is a web-based peer-o-peer service sysem wih mobile nodes providing web services and users (hrough heir mobile devices) invoking web services. Unlike a web service sysem in which nodes are conneced o he Inerne, nodes in service-oriened MANETs are mobile and he communicaion beween peers no wihin radio range is muli-hop wih nodes in he sysem serving as rouers. One can view a service-oriened MANET as an insance of Inerne of Things (IoT) sysems [7] wih a wide range of mobile applicaions including smar-ciy, smar ourism, smar car, smar environmenal monioring, and healhcare [1]. I is paricularly suiable o miliary applicaions where all nodes are mobile wih muli-hop communicaion. This paper discusses our ongoing research work in rus managemen and rus-based algorihm design for serviceoriened MANETs, key ideas and experiences learned, and fuure research direcions. Our aims are o (1) idenify rus dimensions for service-oriened MANET applicaions; (2) develop an efficien and effecive rus proocol for serviceoriened MANETs; and (3) develop efficien and effecive rus-based algorihms for a se of service-oriened MANET applicaions. The overarching principle is he design noion of adapive conrol, allowing rus compuaion, aggregaion, propagaion, formaion (ou of muliple rus dimensions) and updae decisions o be dynamically adjused o minimize rus bias and maximize applicaion performance. This goal is o be achieved in he presence of malicious mobile devices performing a wide range of aacks, including bad-mouhing, ballo-suffing, packe dropping, opporunisic service, selfpromoion, conflicing behavior, and on-off service aacks for personal gain. The res of he paper is organized as follows. Secion II discusses relaed work. Secion III discusses he hrea model for service-oriened MANETs. Secion IV presens our soluions oward rus managemen of mobile devices in service oriened MANETs. Secion V presens our soluions oward rus-based service managemen for performance opimizaion of service-oriened MANET applicaions. Secion VI summarizes key research ideas and experiences learned. Finally, Secion VII concludes he paper and oulines fuure research direcions. II. RELATED WORK Many exising rus models for predicing rus are based on Bayesian inference [3]. Bayesian inference reas rus as a random variable following a probabiliy disribuion (e.g., Bea disribuion) wih is model parameers being updaed upon new observaions. A shorcoming of Bayesian inference is ha rus value does no reveal he uncerainy of rus since i is jus a mean. For example, he same rus value can be given o wo nodes despie one node was observed for jus 2 imes, while he oher node was observed for 20 imes. Belief heory or subjecive logic rus models [9] have been proposed o remedy he problem menioned above, by inroducing uncerainy ino rus calculaion. Fuzzy logic based rus models are also well sudied in he lieraure [12]. Insead of using a binary se, a membership funcion is defined indicaing he degree o which a node is considered rusworhy. Relaive o he works cied above based on Bayesian inference, belief heory, or fuzzy logic, we ake an enirely differen approach. Our roo is in saisical analysis. We develop a regression-based rus model o learn he behavior paern of a SP, aking conex informaion ino consideraion o esimae he reliabiliy rus of a SP ha is seleced by a SR o execue a service reques under a paricular environmen conex. A significan amoun of work has been done in he area of rus-based defenses agains aacks in MANETs [13]- [18], [35]-[38]. A common drawback is ha dynamically uning rus parameers may perform poorly when a node

2 does no have enough self-observaion experiences wih oher nodes in MANET environmens and mus rely on recommendaions. Differen from he works cied above, we advocae he use a robus saisical kernel o olerae false recommendaions o effecively achieve resiliency agains recommendaion aacks. Also unlike exising work, our goal is no o idenify bad SPs, bu o predic wheher a SP, wheher good or bad, can provide good service, given a se of conex variables characerizing he MANET operaional environmen, including dynamically changing opology, limied bandwidh, baery power, and unreliable communicaion. In our approach, a SR learns and predics a SP s service behavior aking conex informaion ino consideraion, insead of jus judging a SP s rusworhiness from self-observaions or recommendaions received, as having been done in exising works. III. THREAT MODEL Jus like Inerne-based web services, in a serviceoriened MANET here are malicious SPs acing for heir own gain. The common goal of malicious nodes is o increase heir chance of being seleced for providing service. Malicious nodes can collude o achieve his common goal. We consider he following malicious aacks in our research: 1. Bad-mouhing aacks: a malicious node can ruin he rus of a good node (by providing bad recommendaions agains i) so as o decrease he chance of ha node being seleced for service. This is a form of collusion recommendaion aacks, i.e., a malicious node can collaborae wih oher malicious nodes o ruin he rus of a good node. 2. Ballo-suffing aacks: a malicious node can boos he rus of anoher malicious node (by providing good recommendaions) so as o increase he chance of ha malicious node being seleced as a SP. This is anoher form of collusion recommendaion aacks, i.e., a malicious node can collaborae wih oher malicious nodes o boos he rus of each oher. 3. Packe-dropping aacks: when serving as a packe relaying node, a malicious node can delay forwarding or simply drop daa packes o ruin he rus of he source node. 4. Opporunisic service aacks: a malicious node can provide good service o gain high repuaion when i senses is rus saus is low, and can provide bad service when i senses is rus saus is high. 5. Self-promoion aacks: A malicious node can boos is service qualiy informaion so as o increase is chance of being seleced as a SP. 6. Conflicing behavior aacks: a malicious node can selecively provide saisfacory service for some SRs while unsaisfacory for ohers. Here, we noe ha a node s bes service qualiy is dicaed by he environmenal and operaional condiions a he ime a service reques is issued. Therefore, a malicious node can only perform conflicing behavior aacks wih a service qualiy no exceeding is bes service qualiy. 7. On-off aacks: insead of always performing is bes service, a malicious node can perform bad service. Wih on-off aacks, a malicious node performs bad service on and off (or randomly) so as o avoid being labeled as a low rus node and risk iself no being seleced as a SP, as well as no being able o effecively perform badmouhing and ballo-suffing aacks. One can view onoff aacks as random aacks. A malicious node may also perform daa modificaion aacks o ruin he repuaion of a good node. We assume daa/source auhenicaion echniques based on PKI can preven such aacks. A malicious node may also jam he communicaion channel or perform denial of service (DoS) aacks o overwhelm a SP. We assume ha sandard inrusion deecion echniques [8] are in place o miigae such aacks. IV. TRUST MANAGEMENT One challenge for implemening rus managemen in service-oriened MANETs is o reliably esimae he rus levels of SPs in a fully disribued manner, in conras wih an e-commerce sysem wih a cenralized auhoriy for rus managemen. Mos exising works ake direc evidence for direc rus assessmen and propagaes is observaions o oher nodes as recommendaions for indirec rus assessmen. However, a malicious node may violae his proocol. Furher, rus managemen of mobile devices mus ake service conex informaion ino consideraion. Such service conex informaion includes he curren capabiliy of a SP (e.g., energy saus), he service environmen (e.g., congesed wireless raffic), he ideniy of he SR (e.g., a friend or a sranger), he payoff obained (which is applicaion-dependen), and he service cos (e.g., energy consumed). All hese facors are called conex variables based on which he service behavior of a node forms a paern. The key o effecive rus managemen is herefore o learn he service behavior paern of a node oward hese conex variables. The behavior paern learned can be used o assess he reliabiliy rus [3] of a SP when i is seleced o service a reques in a paricular conex sae characerized by hese conex variables. More specifically, wihin a specific ype of service, SR i s observaion s ii a ime of he service qualiy received from SP j is eiher saisfacory or unsaisfacory. If he service qualiy is saisfacory, hen s ii =1 and SP j is considered rusworhy; oherwise, s ii =0 and SP j is considered unrusworhy. Le he operaional and environmenal condiions a ime be characerized by a se of disinc conex variables deemed appropriae for an applicaion, denoed by a column vecor x = [x 0,, x m ], where x i represens he ih conex variable. Then, reliabiliy rus or jus rus for shor is he probabiliy ha SP j is capable of providing saisfacory service under he operaional and environmen condiions a ime described by he conex variable se x. Le k (k i) be a recommender who had a prior service experience wih SP j and is asked by SR i o provide is feedback regarding SP j. The recommendaion from node k is in he form of [x, s kk ] specifying he specific operaional and environmenal conex condiions in x under which he

3 observaion in s kk was made. For noaional conveniences, le S ii = s 0 ii,, s n ii, i j, denoe he cumulaive evidence gahered by SR i regarding SP j s service qualiy over [ 0, n ] including self-observaions and recommendaions. Also le X = [x 0,, x n] denoe he corresponding operaional and environmenal conex condiions when he observaions are made. The problem is o learn he service behavior paern of SP j by a laen variable β j beween S ii and X, and predic he probabiliy ha SP j is rusworhy a ime, given he conex environmen se a ime n+1, x n+1, as inpu, i.e., T n+1 i,j = Pr s n+1 ii = 1 x n+1, β j. Essenially T n+1 i,j obained above is he reliabiliy rus of SP j a ime n+1 from SR i s perspecive. The service qualiy a ime n+1, n+1 s ii, can be prediced by seing a rus hreshold, depending on he SR s olerance for he risk. A common pracice is o se he rus hreshold as a value greaer han 0.5. For example, if he rus hreshold is se o be 0.6 by SR i hen he requesed service performed by SP j is prediced o be saisfacory when he prediced reliabiliy rus is greaer han 0.6. In [2], we uilized logi regression as he behavior paern learning mechanism o solve he above rus assessmen problem, resuling in a rus managemen proocol which we call LogiTrus. LogiTrus assesses each SP in erms of is service behavior paerns in response o operaional and environmenal changes characerized by hree conex variables: x e, x c, x p for energy, capabiliy, and price (or reward). Energy is used o measure he cos of ask execuion. In a congesed environmen he probabiliy of wireless channel conenion and signal inerference will be high, so i will cos more for a SP o execue a ask because he SP needs o consume more energy in lisening o he channel and repeaing packe ransmission. The reasons for considering he above conex variables in service-oriened MANET environmens are: (a) a SP is more likely o provide inferior service when he cos of servicing he ask is high (b) a SP is likely o provide inferior service when i is limied in resources and capabiliy; and (c) a profi-aware SP is more likely o provide qualiy service when he SR offers a higher price. SR i will assess he hree conex variables x e, x c, x p while i sends a service reques o SP j as follows: x e is esimaed by he number of neighbors sharing he channel as more energy is consumed for channel conenion and packe reransmission when here are more nodes sharing he channel; x c is esimaed by he packe raffic o SP j as more raffic o SP j hinders is processing capabiliy; x p is SR i s reward o SP j upon saisfacory service compleion. When SP j complees he service, SR i will assess if he service is saisfacory (1) or no (0), and sore he service oucome ogeher wih x e, x c, x p conex informaion as one record in he daase se for learning. I can also pass his experience record o anoher node as a recommendaion. A SR in he sysem uses is own self-observaions and recommendaions received o learn he behavior paern of a SP, and predic he reliabiliy rus of he SP on a service reques in a paricular conex environmen. Relying on is robus learning engine, LogiTrus is highly effecive agains dishones recommendaions (hrough bad-mouhing and ballo-suffing aacks). I significanly ouperforms exising rus compuaion models such as Bea repuaion wih belief discouning [3] and Adapive Trus Managemen [4] in erms of rus accuracy because i akes conex informaion ino consideraion in service behavior assessmen. LogiTrus is also efficien in erms of compuaional complexiy as i uilizes a simple linear model o model he relaion beween conex variables and observaions. Wih conflicing behavior aacks, a SP can selecively provide saisfacory service for some SRs while providing unsaisfacory service for ohers. In general, he relaion beween a SR and a SP deermines he SP s service aiude oward he SR. This is naurally solved by LogiTrus since LogiTrus is based on SR-SP pairing. Tha is, each SR evaluaes each SP based on is own self-observaions and filered recommendaions. If SP j provides bad services o a paricular SR, hen his evidence will be considered by his SR as i learns SP j s behavior paern (ha is, β j ) and will no rus SP j wih is service reques. Wih on-off aacks, a malicious node will aack only randomly so as o evade deecion and avoid being classified as a malicious node. To he sysem, his malicious node is no 100% of he ime providing bad service, bu jus a percenage of ime providing bad service. Therefore, SP j performing on-off aacks ranslaes ino SP j providing bad service only randomly insead of persisenly, which is a paern ha can be learned by SR as LogiTrus learns SP j s behavior paern (ha is, β j ). This in effec allows each SR o cope wih a paricular SP s on-off aack behavior. V. TRUST-BASED ALGORITHM DESIGN FOR APPLICATION PERFORMANCE MAXIMIZATION Service-oriened MANET applicaions are on he rise hanks o he proliferaion of fairly powerful mobile devices and ubiquious wireless echnology. We aim o design and validae rus-based algorihms for applicaion performance maximizaion for service-oriened MANET applicaions wih he goal of saisfying muliple objecives wih conflicing goals o achieve muli-objecive opimizaion (MOO). Trus-based service composiion and binding (wih or wihou MOO) has been sudied in he web services domain bu only a single-rus, i.e., a single dimension of rus, was considered. This largely ignores he fac ha rus is mulidimensional. Idenifying proper rus componens and forming he overall rus ou of muliple rus componens o maximize applicaion performance is of paramoun imporance. We advocae he use of wo key rus dimensions in service reques execuion, namely, compeence and inegriy, as he building blocks of a composie rus meric. Below we discuss our rus-based service managemen

4 algorihm designs for solving wo service-oriened MANET applicaions wih MOO. In [5], we invesigaed a rus-based dynamic ask assignmen algorihm for performing dynamic ask-o-node service assignmens o saisfy muliple objecives wih conflicing goals. The resuls demonsraed ha our rusbased soluion has low complexiy and ye can achieve performance comparable o ha of he ideal soluion wih perfec knowledge of node reliabiliy, and can significanly ouperform he non-rus-based soluion. We analyzed how MOO is achieved by he ideal, rus-based and non-rusbased soluions, and idenified parameer seings under which he rus proocol performance in erms of MOO is opimized for he rus-based soluion which can bes balance muliple objecives wih conflicing goals. The resuls obained are useful for dynamic rus managemen o maximize applicaion performance in erms of MOO in he presence of malicious aacks. In [6], we invesigaed a rus-based service composiion algorihm designed o saisfy mobile user service requess wih muliple objecives including maximizing qualiy-ofservice (QoS) and qualiy-of-informaion (QoI) while minimizing he service cos (e.g., pricing) wih he user saisfacion ulimaely measuring success. Wih a service reques in hand, a SR has o firs formulae a service composiion plan based on he available SPs i encouners and ineracs wih dynamically, and hen deermine he bes node-o-service assignmen for achieving MOO. Dynamic service composiion and binding is especially complicaed in MANETs because of he space-ime complexiy of mobile devices. This issue is furher compounded by he fac ha he informaion received is ofen malicious, erroneous, parly rused, uncerain and incomplee in MANET environmens. Our rus-based service composiion and binding algorihm based on muli-rus ouperforms he non-rus-based counerpar using blacklising, as well as a single-rus-based algorihm using a radiional bea repuaion sysem. Our rus-based algorihm has a linear runime complexiy and is able o achieve a soluion qualiy approaching ha generaed by Ineger Linear Programming wihou sacrificing much soluion accuracy. We conduced a comparaive performance analysis of single-rus vs. mulirus proocols for peer-o-peer rus evaluaion in serviceoriened MANETs. We uilized rus o effecively preven malicious nodes from disruping he operaion of a serviceoriened MANET. We conduced a deailed performance analysis and demonsraed ha our rus-based algorihm can effecively penalize malicious nodes performing badmouhing, ballo-suffing packe dropping, self-promoion, or opporunisic service aacks, hus filering ou malicious nodes from service paricipaion, and can ulimaely lead o high user saisfacion. VI. KEY IDEAS AND EXPERIENCES LEARNED The major difference beween a service-oriened MANET and an Inerne-based web service sysem is ha he informaion received in MANET environmens is ofen malicious, erroneous, parly rused, uncerain and incomplee. In his paper we discussed key research ideas for rus-based service managemen of mobile devices in service-oriened MANETs wherein every node can be a service provider or a service requeser. The firs key idea is o ake special characerisics of service-oriened MANET environmens ino consideraion so as o design an efficien and effecive rus proocol. We discussed a novel logi regression-based rus model called LogiTrus o dynamically esimae he rus of a mobile device based on how i behaves in response o dynamically changing MANET environmens characerized by a se of conex variables. LogiTrus ouperforms radiional approaches based on Bayesian Inference wih belief discouning in erms of rus accuracy and resiliency agains aacks, while mainaining a low false posiive rae. I is efficien as i adops a simple liner model for behavior learning wih low compuaional complexiy. I is effecive since i reflecs dynamic MANET characerisics, such as limied bandwidh and baery power, as conex variables in he learning model formulaion. The 2nd key idea is o use muli-rus insead of singlerus for rus-based algorihm design, recognizing mulidimensional rus assessmen is criical for decision makings. The 3rd key idea is ha muli-rus-based algorihm design is applicaion specific. One mus apply he bes rus formaion ailored o he applicaion requiremens o achieve applicaion performance maximizaion, especially for hose applicaions wih muli-objecive opimizaion goals. We demonsraed ha our muli-rus-based algorihm ouperforms is non-rus-based and single-rus-based counerpars wih muli-objecive opimizaion over a range of service-oriened MANET applicaions, including nodeo-service composiion and binding, and node-o-ask assignmen MANET applicaions. Furhermore, we demonsraed ha our muli-rus-based algorihms for solving hese problems are efficien (wih linear runime complexiy) and effecive wihou compromising soluion opimaliy, when compared wih non-rus-based soluions, and oher single-rus-based soluions based on Bayesian inference. VII. FUTURE RESEARCH DIRECTIONS There are several fuure research direcions for rus managemen of mobile devices in service-oriened MANETs: Firs, we plan o address he issue of runime learning and decision making for MANET nodes wih limied sorage and compuaion resources. This may involve he use of heurisics for each resource-limied node o sore mos relevan rus records [10]. Second, we plan o incorporae adapive conrol o he rus proocol design. A possible direcion is o use a recommendaion filering mechanism o dynamically decide if a recommendaion is o be aken or no. Adapive conrol may be achieved by adjusing he recommender filering hreshold value based on he hosiliy level in he environmen. When he hosiliy level is low (i.e., no many bad nodes are ou here), one can se a low hreshold so as o ake in recommendaions ino he daase, because chances are all recommendaions are benign. On he oher

5 hand, when he hosiliy level is high, one can se a high hreshold o filer ou false recommendaions so as no o conaminae he daase for effecive behavior learning. Third, alhough we have refleced MANET environmen characerisics such as limied bandwidh and energy power as conex variables in our rus model formulaion, we have no considered node social behaviors which can also be reaed as conex informaion. A conex variable such as friendship can dicae wheher a node will perform good service or bad service oward anoher node, or if a node will perform ballo-suffing or bad-mouhing aack oward anoher node. We plan o furher es he resiliency of LogiTrus [2] agains more complicaed environmenal and operaional scenarios such as noisy environmens, socialbased service behaviors, as well as more sophisicaed aack behaviors such as opporunisic, collusion and insidious aacks [11]. Lasly, we plan o leverage game heory and arificial inelligence principles [19]-[23], and sochasic Peri ne modeling echniques [24]-[34] o capure he dynamics beween aacker/defense behaviors [39]-[42] and reason how a service requeser can perform couneraacks by adapive rus-based service managemen for achieving muli-objecive opimizaion of service qualiy. ACKNOWLEDGMENT This work is suppored in par by he U. S. Army Research Laboraory and he U. S. Army Research Office under conrac number W911NF This research was also parially suppored by he Deparmen of Defense (DoD) hrough he office of he Assisan Secreary of Defense for Research and Engineering (ASD (R&E)). The views and opinions of he auhor(s) do no reflec hose of he DoD or ASD (R&E). REFERENCES [1] E. Borgia, "The Inerne of Things vision: Key feaures, applicaions and open issues," Compuer Communicaions, vol. 54, 2014, pp [2] Y. Wang, Y. C. Lu, I. R. Chen, J. H. Cho, A. Swami, and C. T. 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