Managing Trust Relationships in Peer 2 Peer Systems

Transcription

1 Managing Trust Rlationships in Pr 2 Pr Systms R.S.SINJU PG STUDENT, DEPARTMENT OF COMPUTER SCIENCE, PONJESLY COLLEGE OF ENGINEERING NAGERCOIL, TAMILNADU, INDIA C.FELSY ASST.PROF, DEPARTMENT OF COMPUTER SCIENCE, PONJESLY COLLEGE OF ENGINEERING, NAGERCOIL, TAMILNADU, INDIA Abstract: Pr-2-Pr systm xposs thm to malicious activity. Pr-2-Pr systm mans computr in th systm can act as both clint and srvr. In a Pr-2-Pr ntwork, th prs ar computr systms which ar connctd to ach othr via th intrnt. Fils can b shard dirctly btwn systms on th ntwork without th nd of a cntral srvr. Building trust rlationships among prs can dcras th attacks of malicious prs. A good pr uploads authntic fils and givs fair rcommndations. A malicious pr prforms both srvic and rcommndation-basd attacks. Uploading a virus infctd (or) an inauthntic fil is a srvic basd attack. Giving a mislading rcommndation intntionally is a rcommndation basd attack. Slf Organizing Trust Modl (SORT) dtcts th srvic basd attack and rcommndation basd attack. If on pr wants to upload/download fil from anothr pr mans pr will snd th qury to pr that intractd in th past for larn th trust information of othr prs. So, nighbouring nod will giv th rcommndation to pr. Basd on th rcommndation only Pr dcids whthr th nod is good (or) malicious. Find th nod is malicious nod mans pr will not intract with malicious nod. Isolat th malicious nod from th ntwork. Find th nod is good mans pr intract with good pr. Pr stors a sparat history of intractions for ach Acquaintanc. Kywords: Pr-to-pr systms, trust managmnt, rputation, scurity. I. INTRODUCTION Opn natur of pr-to-pr systms xposs thm to malicious activity. Building trust rlationships among prs can mitigat attacks of malicious prs. This papr prsnts distributd algorithms that nabl a pr to rason about trust worthinss of othr prs basd on past intractions and rcommndations. Prs crat thir own trust ntwork in thir proximity by using local information availabl and do not try to larn global trust information. Two contxts of trust, srvic, and rcommndation contxts ar dfind to masur trustworthinss in providing srvics and giving rcommndations. Intractions and rcommndations ar valuatd basd on importanc, rcntnss, and pr satisfaction paramtrs. Additionally, rcommndr s trustworthinss and confidnc about a rcommndation ar considrd whil valuating rcommndations. Simulation xprimnts on a fil sharing application show that th proposd modl can mitigat attacks on 16 diffrnt malicious bhavior modls. In th xprimnts, good prs wr abl to form trust rlationships in thir proximity and isolat malicious prs. Pr to Pr (P2P) systms rly on collaboration of prs to accomplish tasks. Eas of prforming malicious activity is a thrat for scurity of P2P systms. Crating long-trm trust rlationships among prs can provid a mor scur nvironmnt by rducing risk and uncrtainty in futur P2P intractions. Howvr, stablishing trust in an unknown ntity is difficult in such a malicious nvironmnt. Furthrmor, trust is a social concpt and hard to masur with numrical valus. Mtrics ar ndd to rprsnt trust in computational modls. Classifying prs as ithr trustworthy or untrustworthy is not sufficint in most cass. Mtrics should hav prcision so prs can b rankd according to trustworthinss. Intractions and fdbacks of prs provid information to masur trust among prs. Intractions with a pr provid crtain information about th pr but fdbacks might contain dcptiv Pag 29

2 information. This maks assssmnt of trustworthinss a challng. In th prsnc of an authority, a cntral srvr is a prfrrd way to stor and manag trust information..g., Bay. Th cntral srvr scurly stors trust information and dfins trust mtrics. Sinc thr is no cntral srvr in most P2P systms, prs organiz thmslvs to stor and manag trust information about ach othr [2], [3]. Managmnt of trust information is dpndnt to th structur of P2P ntwork. Managing trust is a problm of particular importanc in pr-to-pr nvironmnts whr on frquntly ncountrs unknown agnts. Existing mthods for trust managmnt that ar basd on rputation focus on th smantic propr- tis of th trust modl. Thy do not scal as thy ithr rly on a cntral databas or rquir maintaining global knowldg at ach agnt to provid data on arlir intractions. In this papr w prsnt an approach that addrsss th problm of rputation-basd trust managmnt at both th data managmnt and th smantic lvl. W mploy at both lvls scalabl data structurs and algorithms that rquir no cntral control and allow assssing trust by computing an agnts rputation from its formr intractions with othr agnts. Thr ar no wll dfind mthods for managing trust rlationships in p2p systms. Th DHT basd approachs ar only suitd for structurd p2p ntworks not for unstructurd p2p ntworks. Som of th xisting mthods introduc cntral authority in p2p ntworks which may collaps p2p natur. Evry agnt must kp rathr complx and vry larg data structurs that rprsnt a kind of global knowldg about th whol ntwork. This papr prsnts distributd algorithms that nabl a pr to rason about trustworthinss of othr prs basd on past intractions and rcommndations. Prs crat thir own trust ntwork in thir proximity by using local information availabl and do not try to larn global trust information. Two contxts of trust, srvic, and rcommndation contxts ar dfind to masur trustworthinss in providing srvics and giving rcommndations. Slf-Organizing Trust modl (SORT) that aims to dcras malicious activity in a P2P systm by stablishing trust rlations among prs in thir proximity. In SORT, prs ar assumd to b strangrs to ach othr at th bginning. A pr bcoms an acquaintanc of anothr pr aftr providing a srvic,.g., uploading a fil. If a pr has no acquaintanc, it chooss to trust strangrs. An acquaintanc is always prfrrd ovr a strangr if thy ar qually trustworthy. Using a srvic of a pr is an intraction, which is valuatd basd on wight (importanc) and rcntnss of th intraction, and satisfaction of th rqustr. An acquaintanc s fdback about a pr, r commndation, is valuatd basd on rcommndr s trust worthinss. It contains th rcommndr s own xprinc about th pr, information collctd from th rcommndr s acquaintancs, and th rcommndr s lvl of confidnc in th rcommndation. If th lvl of confidnc is low, th rcommndation has a low valu in valuation and affcts lss th trustworthinss of th rcommndr. SORT dfins thr trust mtrics. Rputation mtric is calculatd basd on rcommndations. It is important whn dciding about strangrs and nw acquaintancs. Rputation loss its importanc as xprinc with an acquaintanc incrass. Srvic trust and rcommndation trust ar primary mtrics to masur trustworthinss in th srvic and rcommndation contxts, rspctivly. Th srvic trust mtric is usd whn slcting srvic providrs. Th rcommndation trust mtric is important whn rqusting rcommndations. Whn calculating th rputation mtric, rcommndations ar valuatd basd on th rcommndation trust mtric. On pr is markd as trustd by SORT and if it is turnd OFF from ntwork, thr is a possibility to anothr malicious pr taks its position and act as trustd pr. This can b avoidd by Auto Updat Mchanism. II. RELATED WORK AND CONTRIBUTION Th simulation runs as cycls. Each cycl rprsnts a priod of tim. Downloading a fil is an intraction. A pr sharing fils is calld an uploadr. A pr downloading a fil is calld a downloadr. Th st of prs who downloadd a fil from a pr ar calld downloadrs of th pr. An ongoing download/ upload opration is calld a sssion. Simulation paramtrs ar gnratd basd on rsults of svral mpirical studis [6], [7] to mak obsrvations ralistic. A fil sarch rqust rachs up to 40 prcnt of th ntwork and rturns onlin uploadrs only. A fil is downloadd from on uploadr to simplify intgrity chcking. All prs ar assumd to hav antivirus softwar so thy can dtct infctd fils Four diffrnt cass ar studid to undrstand ffcts of trust calculation mthods undr attack conditions: No trust. Trust information is not usd for uploadr slction. An uploadr is slctd according to its bandwidth. This mthod is th bas cas to undrstand if trust is hlpful to mitigat attacks. Pag 30

3 No rputation qury. An uploadr is slctd basd on trust information but prs do not rqust rcommndations from othr prs. Trust calculation is don basd on SORT quations but rputation (r) valu is always zro for a pr. This mthod will hlp us to assss if rcommndations ar hlpful. SORT. In SORT, prs ar assumd to b strangrs to ach othr at th bginning. A pr bcoms an acquaintanc of anothr pr aftr providing a srvic,.g., uploading a fil. If a pr has no acquaintanc, it chooss to trust strangrs. Flood rputation qury. SORT quations ar usd but a rputation qury is floodd to th whol ntwork. This mthod will hlp us to undrstand if gtting mor rcommndations is hlpful to mitigat attacks. In SORT, to valuat intractions and rcommndations bttr, importanc, rcntnss, and pr satisfaction paramtrs ar considrd. Rcommndr s trustworthinss and confidnc about rcommndation ar considrd whn valuating rcommndations. Additionally, srvic and rcommndation contxts ar sparatd. This nabld us to masur trustworthinss in a wid varity of attack scnarios. Most trust modls do not considr how intractions ar ratd and assum that a rating mchanism xists. In this study, w suggst an intraction rating mchanism on a fil sharing application and considr many ral-lif paramtrs to mak simulations mor ralistic. A good pr uploads authntic fils and givs fair rcommndations. A malicious pr (attackr) prforms both srvic and rcommndation-basd attacks. Four diffrnt attack bhaviors ar studid for malicious prs: naiv, discriminatory, hypocritical, and oscillatory bhaviours. A non-malicious ntwork consists of only good prs. A malicious ntwork contains both good and malicious prs. Th satisfaction paramtr is calculatd basd on following variabls: Th ratio of avrag bandwidth (AvBw) and agrd bandwidth (AgrBw) is a masur of rliability of an uploadr in trms of bandwidth. Th ratio of onlin (OnP) and offlin (OffP) priods rprsnts availability of an uploadr. A. Ntwork architctur NODE A NODEB(Offlin ) NODE D NODE C(malicious) SORT REPUTATION METRIC SERVICE TRUST METRIC RECOMMENDATION TRUST METRIC COMPETENCE BELIEF INTEGRITY BELIEF Pi 1.rcommn dation rq about Pj Pk DETECT MALICIOUS NODE(Rcommndation attack-50%,srvic basd attack-100%) AUTO UPDATE MECHANISM DETECT MALICIOUS NODE 3. S r v i c r q u s t 4. s r v i c 2.rcommnda tion about Pj Pj Fig a: Architctur Diagram Pag 31

4 Downloading a fil is an intraction. A pr sharing fils is calld an uploadr. A pr downloading a fil is calld a downloadr. Th st of prs who downloadd a fil from a pr ar calld downloadrs of th pr. An ongoing download/ upload opration is calld a sssion. A good pr uploads authntic fils and givs fair rcommndations. A malicious pr (attackr) prforms both srvic and rcommndation-basd attacks. Four diffrnt attack bhaviours ar studid for malicious prs: naiv, discriminatory, hypocritical, and oscillatory bhaviours. A non malicious ntwork consists of only good prs. A malicious ntwork contains both good and malicious prs. SORT dfins thr trust mtrics. Rputation mtric is calculatd basd on rcommndations. It is important whn dciding about strangrs and nw acquaintancs. Rputation loss its importanc as xprinc with an acquaintanc incrass. Srvic trust and rcommndation trust ar primary mtrics to masur trustworthinss in th srvic and rcommndation contxts, rspctivly. Th srvic trust mtric is usd whn slcting srvic providrs. Th rcommndation trust mtric is important whn rqusting rcommndations. Whn calculating th rputation mtric, rcommndations ar valuatd basd on th rcommndation trust mtric. Assum that pi wants to gt a particular srvic. pj is a strangr to pi and a probabl srvic providr. To larn pj s rputation, pi rqusts rcommndations from its acquaintancs. Assum that pk snds back a rcommndation to pi. Aftr collcting all rcommndations, pi calculats rij. Thn, pi valuats pk s rcommndation, stors rsults in RHik, and updats rtik. Assuming pj is trustworthy nough, pi gts th srvic from pj. Thn, pi valuats this intraction and stors th rsults in SHij, and updats stij. On pr is markd as trustd by SORT and if it is turnd off from ntwork,thr is a possibility to anothr malicious pr taks its position and act as trustd pr.this can b avoidd by th Auto updat mchanism. B. Algorithm Dsign and Implmntation Topology cration is crating a ntwork and maintaining communication among various nods in pr to pr ntwork which hlps us to shar th data. Crat diffrnt nods in propr nam, ip addrss and port numbr for data communication. Th nod is addd to giv th nam of th nod, ip addrss and port addrss of that nod. If th ntir nod adds succssfully to display th nod connction frams. Crating long-trm trust rlationships among prs can provid a mor scur nvironmnt by rducing risk and uncrtainty in futur P2P intractions. Howvr, stablishing trust in an unknown ntity is difficult in such a malicious nvironmnt. Furthrmor, trust is a social concpt and hard to masur with numrical valus. Mtrics ar ndd to Pag 32

5 rprsnt trust in computational modls. Classifying prs as ithr trustworthy or untrustworthy is not sufficint in most cass. Mtrics should hav prcision so prs can b rankd according to trustworthinss. Intractions and fdbacks of prs provid information to masur trust among prs. Intractions with a pr provid crtain information about th pr but fdbacks might contain dcptiv information. This maks assssmnt of trustworthinss a challng. Slf-Organizing Trust modl (SORT) that aims to dcras malicious activity in a P2P systm by stablishing trust rlations among prs. Each pr dvlops its own local viw of trust about th prs intractd in th past. In this way, good prs form dynamic trust groups in thir proximity and can isolat malicious prs. In SORT, prs ar assumd to b strangrs to ach othr at th bginning. A pr bcoms an acquaintanc of anothr pr aftr providing a srvic,.g., uploading a fil. If a pr has no acquaintanc, it chooss to trust strangrs. SORT dfins thr trust mtrics. Rputation mtric is calculatd basd on rcommndations. It is important whn dciding about strangrs and nw acquaintancs. Rputation loss its importanc as xprinc with an acquaintanc incrass. Srvic trust and rcommndation trust ar primary mtrics to masur trustworthinss in th srvic and rcommndation contxts, rspctivly. Th srvic trust mtric is usd whn slcting srvic providrs. Th rcommndation trust mtric is important whn rqusting rcommndations. Whn calculating th rputation mtric, rcommndations ar valuatd basd on th rcommndation trust mtric. Crating trust rlationship is basd upon two contxts of trust. Thy ar Srvic Contxt, Rcommndation Contxt. Th srvic trust mtric is usd whn slcting srvic providrs. Th rcommndation trust mtric is important whn rqusting rcommndations. Whn pi sarchs for a particular srvic, it gts list of srvic providrs. Considring a fil sharing application, pimay download a fil from ithr on or multipl uploadrs. With multipl uploadrs, chcking intgrity is a problm sinc any fil part downloadd from an uploadr might b inauthntic.. Assum that pi wants to gt a particular srvic. pj is a strangr to pi and a probabl srvic providr. To larn pj s rputation, pi rqusts rcommndations from its acquaintancs. Assum that pk snds back a rcommndation to pi. Aftr collcting all rcommndations, pi calculats rij. Thn, pi valuats pk s rcommndation, stors rsults in RHik, and updats rtik. Assuming pj is trustworthy nough, pi gts th srvic from pj. Thn, pi valuats this intraction and stors th rsults in SHij, and updats stij. III. PERFORMANCE ANALYSIS AND RESULTS A. Prformanc Analysis Fig b: Existing Systm(10% Malicious) Pag 33

6 Fig c: Proposd Systm (50% Malicious) Analysis on Individual Attackrs This sction xplains th rsults of xprimnts on individual attackrs. For ach typ of individual attackr, two sparat ntwork topologis ar cratd: on with 10 prcnt malicious and on with 50 prcnt malicious. Each ntwork topology is tstd with four trust calculation mthods. In th xprimnts, a hypocritical attackr bhavs malicious in 20 prcnt of all intractions. A discriminatory attackr slcts 10 prcnt of all prs as victims. An oscillatory attackr bhavs good for 1,000 cycls and malicious for 100 cycls. Srvic-basd attacks Tabl shows th prcntag of srvic-basd attacks prvntd by ach trust calculation mthod. Whn a malicious pr uploads an infctd/ inauthntic fil, it is rcordd as a srvic-basd attack. Numbr of attacks in No Trust mthod is considrd as th bas cas to undrstand how many attacks can happn without using trust information. Thn, numbr of attacks obsrvd for ach trust calculation mthod is compard with th bas cas to dtrmin th prcntag of attacks prvntd. In th tabl, NoRQ and FloodRQ dnot No rputation qury and Flood rputation qury mthods, rspctivly. Rcommndation-basd attacks In th simulations, whn a malicious pr givs a mislading rcommndation, it is rcordd as a rcommndation-basd attack. Fig. 2 shows th rat of rcommndation-basd attacks in th 10 prcnt malicious ntwork. Whn SORT is usd, prs form thir own trust ntwork with tim and do not rqust rcommndations from untrustworthy prs. Thrfor, SORT can ffctivly mitigat rcommndation-basd attacks with tim. In FloodRQ mthod, prs collct mor rcommndations from both acquaintancs and strangrs. Distribution of trust mtrics Prs with highr capabilitis (ntwork bandwidth, onlin priod, and numbr of shard fils) can finish mor intractions succssfully. Thus, thy gnrally hav bttr rputation and srvic trust valus. Rcommndation trust valus ar not dirctly rlatd to pr capabilitis sinc giving a rcommndation dos not rquir high capabilitis. Analysis on Individual Psudospoofrs This sction xplains th rsults of xprimnts on individual psudospoofrs. Psudospoofrs chang thir psudonyms aftr vry 1,000 cycls. Srvic-basd attacks Tabl shows th attack prvntion ratio for individual psudospoofrs. Th valus obtaind by comparing th bas cas with ach trust calculation mthod. Aftr vry psudonym chang, attackrs bcom strangrs to othrs. This bhavior has two ffcts: 1) Psudospoofrs clar thir bad history. Hnc a good pr may intract with thm whn it cannot find mor rliabl uploadrs, which incrass attacks. 2) Psudospoofrs bcom mor isolatd from good prs. Thy los thir ability to attract good prs with tim, which dcrass attacks. Pag 34

7 Rcommndation-basd attacks Comparing to nonpsudonym changing individual attackrs, rcommndation-basd attacks dcras du to th scond ffct. Attackrs bcom mor isolatd from good prs aftr vry psudonym chang and gt lss rcommndation rqusts. Thrfor, thir rcommndation-basd attacks sharply dcras aftr vry psudonym chang in a 10 prcnt malicious ntwork. This situation is slightly diffrnt in a 50 prcnt malicious ntwork. Th good prs nd to intract with mor strangrs sinc thy can hardly find ach othr. Hnc attackrs can distribut mor mislading rcommndations. Distribution of trust mtrics Sinc psudospoofrs clar thir old history in vry 1,000 cycls, thy cannot gain a high rputation among good prs. This situation is sam for srvic trust and rcommndation trust mtrics. Avrag rputation, srvic trust, and rcommndation trust valus of psudospoofrs rmain undr 0.01 valu in most simulations. Analysis on Collaborators Collaboration of attackrs gnrally maks attack prvntion hardr. This sction prsnts xprimntal rsults on collaborators. Collaborators form tams of siz 50 and launch attacks as tams. W first trid tams of siz 10 but this was not nough to bnfit from collaboration and rsults wr clos to individual attackrs. Hnc tam siz is incrasd to obsrv ffcts of collaboration bttr. Srvic-basd attacks Tabl shows th prcntag of attacks prvntd by ach mthod. Attacks of naïv collaborators can b prvntd by 60 prcnt or mor. Naiv collaborators ar idntifid by good prs aftr th first intraction so thy ar not askd for rcommndations. Rcommndation Basd Attacks In a 50 prcnt malicious ntwork, attacks of hypocritical and oscillatory collaborators can b containd on a lvl but cannot b dcrasd to an accptabl lvl. Thy can continu to dissminat mislading rcommndations du to thir larg numbr. Discriminatory collaborators can dissminat mor mislading rcommndations than othrs sinc thy ar trustd by 90 prcnt of all prs. Discriminatory collaborators constitut 50 prcnt of all pr population whil victims ar 10 prcnt of all population. Trust Mtric Good prs can maintain highr rputation than hypocritical collaborators in th 10 prcnt malicious ntwork. In 50 prcnt malicious ntwork stup, collaborators gain highr rputation valus and dcras rputation of good pr. Analysis on Collaborating Pdsudospoofrs This sction prsnts th rsults of xprimnts on collaborating psudospoofrs. Collaborating psudospoofrs ar assumd to chang thir psudonyms in vry 1,000 cycls using an xtrnal synchronization mthod. Srvic-basd attacks Tabl shows th prcntag of attacks prvntd by ach trust calculation mthod. Th rsults vrify our obsrvations. In naiv and discriminatory bhaviors, changing psudonym causs th first ffct: collaborators clar bad history and gt mor attack opportunitis. Thrfor, attack prvntion ratio drops for ths collaborators. Rcommndation-basd attacks As in individual psudospoofrs, collaborating psudospoofrs ar isolatd mor from good prs aftr vry psudonym chang. Thy gt lss rcommndation rqusts and thus thy can do narly zro rcommndation-basd attacks in 10 prcnt malicious ntwork. Trust mtrics Lik individual psudospoofrs, collaborating psudospoofrs cannot gain high rputation, srvic trust, or rcommndation trust valus sinc thy los rputation aftr vry psudonym chang. Du to thir low rcommndation Pag 35

8 trust valus, collaborators ar not askd for rcommndations by good prs. Thrfor, thy can distribut small numbr of mislading rcommndations. B. Rsults Fig d: Mssag Communication Th data can b communicatd ffctivly without any malicious activity.th Slf Organizing Trust Modl finds th malicious nod by itslf with srvic and rcommndation contxt.so that th communication is mad mor ffctiv without th attack of malicious nod in th ntwork.stting B as 'malicious Nod'.Snd data from 'a' to 'c'.hr th mssag is only Rcivd to 'b'. Sinc 'B' is malicious.this path is addd as invalid. Now I am going to snd mssag from 'D' to B.Hr th mssag rcivd to 'B' Sinc th intrmdiat nod 'C' is not malicious. Th path is addd as valid. IV. CONCLUSION SORT mitigatd both srvic and rcommndation-basd attacks in most xprimnts. Howvr, in xtrmly malicious nvironmnts such as a 50 prcnt malicious ntwork, collaborators can continu to dissminat larg amount of mislading rcommndations. Anothr issu about SORT is maintaining trust all ovr th ntwork. Ths issus might b studid as a futur work to xtnd th trust modl. Using trust information dos not solv all scurity problms in P2P systms but can nhanc scurity and ffctivnss of systms. REFERENCES [1] AhmtBurakCan and Bharat(2013), A Slf-Organizing Trust Modl for Pr-to-Pr Systms IEEE Trans.Dpndabl and Scur Computing,vol 10,No.1. [2] Abrr.K and Dspotovic.Z(2001), Managing Trust in a Pr-2-Pr Information Systm Proc. 10th Intl Conf. Information and Knowldg Managmnt (CIKM). [3] Kamvar.S, Schlossr.M, and Garcia-Molina.H,(2003) Th (Eigntrust) Algorithm for Rputation Managmnt in P2P Ntworks Proc. 12th World Wid Wb Conf. (WWW). [4] SlcukA.A,Uzun.E, and Parint.M.R(2004), A Rputation-Basd Trust Managmnt Systm for P2P Ntworks Proc. IEEE/ACM Fourth Int l Symp. Clustr Computing and th Grid (CCGRID). [5] Zhou. R, Hwang. K, and Cai. M(2008), Gossiptrust for Fast Rputation Aggrgation in Pr-to-Pr Ntworks IEEE Trans. Knowldg and Data Eng., vol. 20, no. 9. [6] Abdul-Rahman. A and Hails.S(2008), Supporting Trust in Virtual Communitis Proc. 33rd Hawaii Int l Conf. Systm Scincs (HICSS). [7] Yu. B and Singh.M(2000), A Social Mchanism of Rputation Managmnt in Elctronic Communitis Proc. Cooprativ Information Agnts (CIA). Pag 36