A Trust Model Based on Cloud Model and Bayesian Networks

Transcription

1 Avalable onlne at roceda Envronmental Scences (20) A Trust Model Based on Cloud Model and Bayesan Networs Bo Jn a,yong Wang b, Zhenyan Lu b, Jngfeng Xue b a Key Lab of Informaton Networ Securty of Mnstry of ublc Securty(The Thrd Research Insttute of Mnstry of ublc Securty),Shangha, Chna,jnbo@stars.org.cn b School of Software,Bejng Insttute of Technology Bejng, Chna,wangyong@bt.edu.cn Abstract the Internet has been becomng the most mportant nfrastructure for dstrbuted applcatons whch are composed of onlne servces. In such open and dynamc envronment, servce selecton becomes a challenge. The approaches based on subjectve trust models are more adaptve and effcent than tradtonal bnary logc based approaches. Most well nown trust models use probablty or fuzzy set theory to hold randomness or fuzzness respectvely. Only cloud model based models consder both aspects of uncertanty. Although cloud model s deal for representng trust degrees, t s not effcent for context aware trust evaluaton and dynamc updates. By contrast, Bayesan networ as an uncertan reasonng tool s more effcent for dynamc trust evaluaton. An uncertan trust model that combnes cloud model and Bayesan networ s proposed n ths paper. 20 ublshed by Elsever Ltd. Open access under CC BY-NC-ND lcense. Selecton and/or peer-revew under responsblty of the Intellgent Informaton Technology Applcaton Research Assocaton. Keywords:trust model; cloud model;bayesan networ; context aware; unceratnty.introducton In recent years, the popularty of the Internet boosts many new concepts such as cloud computng, The Internet of Thngs, and Internetware. The common dea of these concepts s servce-orented, whch means that the dstrbuted applcatons are constructed based on ndependent component servces wth standard nterfaces []. How to select trustworthy servces automatcally becomes the ey part of software development. Tradtonal securty approaches such as authentcaton s bnary logc, whch cannot present multple trust degrees or the human s servce selecton prncple of good enough. The subjectve trust models can mae up for the defcences by reasonng about a servce s trustworthness n future nteractons accordng to one entty s drect nteractons wth that entty and recommendatons (ratngs) from other enttes. Trust s a concept wth many uncertantes, among whch, randomness and fuzzness are the two most mportant uncertantes. To grasp the uncertanty of trust accurately, most well nown trust models use ublshed by Elsever Ltd. Open access under CC BY-NC-ND lcense. Selecton and/or peer-revew under responsblty of the Intellgent Informaton Technology Applcaton Research Assocaton. do:0.06/j.proenv

2 Bo Jn et al. / roceda Envronmental Scences (20) probablty or fuzzy set theory to hold randomness or fuzzness respectvely. Although the randomness and fuzzness are qute dfferent n nature, many lngustc concepts contan smultaneous randomness and fuzzness. Keepng ths n mnd, L et al. proposed a new cogntve model- Cloud model [2], whch can synthetcally descrbe the randomness and fuzzness of concepts and mplement the uncertan transformaton between a qualtatve concept and ts quanttatve nstantatons. Durng recent years, several researchers proposed cloud model based trust models n order to consder both randomness and fuzzness n trust evaluaton. Although cloud model s deal for representng uncertan trust value, ts logcal operatons and algebra operatons lac sound theoretcal bass. As the result, all exstng trust models [3], [4], [5] based on cloud model have two common shortcomngs n the aspect of trust aggregaton (.e. an agent usually estmates an unnown servce entty s trust value by aggregatng recommendatons from other relable agents.). Frst, the trust aggregaton reles on cloud algebra operatons, whch s n fact the weghted average of cloud numercal parameters, and all weghts are preset accordng to the trustor s experence, whch cannot reflect the dfference between recommendatons and the ratngs gven by the source trustor, and causes to reduce the aggregaton accuracy. Second, they cannot resst malcous recommendaton attacs, because the relablty of recommendatons s not evaluated and all recommendatons are treated equally. In addton, these models don t consder context nformaton, whch maes t mpossble to evaluate trust values and mae decsons accordng to context nformaton. Our prevous research wor shows that Bayesan networs can help mae context aware trust evaluaton and aggregaton n a ratonal (sound theoretcal bass), ntutve (graphcal representaton) and robust (malcous recommendaton attacs resstant) way [6]. So, we propose a trust model to combne cloud model and Bayesan networs, whch can represent and evaluate the uncertanty of trust more accurately and effcently. 2.Trust representaton usng cloud model 2..Cloud Model Gven a qualtatve concept T de ned over a unverse of dscourse U, let x U s a random nstantaton of the concept T and μt(x) [0,] s the certanty degree of x belongng to T,whch corresponds to a random number wth a steady tendency. Then, the dstrbuton of x n the unverse U can be de ned as a cloud and x can be called as a cloud drop. A cloud descrbes the overall quanttatve property of a concept by the three numercal characterstcs as follows: Expectaton Ex s the mathematcal expectaton of the cloud drops belongng to a concept n the unversal. Entropy En represents the uncertanty measurement of a qualtatve concept. It s determned by both the randomness and the fuzzness of the concept. In one aspect, as the measurement of randomness, En re ects the dspersng extent of the cloud drops and n the other aspect, t s also the measurement of fuzzness, representng the scope of the unverse that can be accepted by the concept. Hyperentropy He s the uncertan degree of entropy En. 2.2.Trust Cloud We use cloud to represent subjectve trust, called trust cloud. The unverse of dscourse U= [0, n], n s any postve nteger. Trust T s a qualtatve concept defned over U. Because trust for a servce entty s

3 454 Bo Jn et al. / roceda Envronmental Scences (20) evaluated from ratngs from raters or recommenders, any ratng r U can be regarded as a random nstantaton of T. Every r s a cloud drop of trust cloud, whch means a quanttatve nstantaton of the qualtatve concept T. The certanty degree of r belongng to T s denoted byμt(r) [0, ]. Besdes of cloud drops, we can also descrbe the overall quanttatve property of T by Ex, En, and He. That s to say, the overall trust for a servce entty can be represented usng a tuple T (Ex, En, He). Next, we wll descrbe brefly how to compute Ex, En, He, and μt(r) from all r. In real ratng systems, t s common to rate servce qualty usng dscrete satsfactory levels (level,,leveln). Each level represents the extent to whch an agent s satsfed wth the nteracton, n whch level means extremely unsatsfed and leveln means extremely satsfed. A ratng r can be any nteger n set I= {, 2,, n}, obvously, I U. We use a Bayesan networ to calculate Ex, whch taes all cloud drops r together wth the context nformaton as evdence, and the expectaton of the cloud drops s Ex. Ex changes when new ratng r s taen, and the newer the ratng, the closer t s to the real value of Ex. So we adopt a tme decay mechansm to mae newer ratngs effect on Ex stronger. lease refer to art III for the detals of Ex calculaton. As to entropy En calculaton, L et.al use the standard devaton or the frst-order absolute central moment of all cloud drops [7], we mprove the algorthm by consderng tme decay n Ex calculaton, as n En = r j= j Ex, 2. () Hyperentropy He s calculated as the frst-order absolute central moment of all En, as n He = En j j= = En, 3. (2) The certanty degree of r belongng to concept T s calculated usng (3), n whch Ex and En s the current value of expectaton and entropy respectvely. T ( r ) ( r Ex) 2 2En 2 μ = e. (3) 3.Trust evaluaton usng bayesan networs Subject trust s a context-specfc concept, but nether exstng trust models based on cloud model consder context nformaton explctly. The man reason s that t s not easy to ntegrate context nformaton nto cloud algebra and logc operatons, whch all tae the overall three numercal characterstcs as operands. If consderng complex compound context nformaton, the case would be even worse. Our prevous wor [6] shows that Bayesan networs can be used to ntegrate context nformaton nto trust evaluaton to mprove accuracy. In ths secton, we wll descrbe how to combne the trust cloud descrbed n prevous secton wth Bayesan networs to form a context-aware uncertan trust model.

4 Bo Jn et al. / roceda Envronmental Scences (20) Basc Context-aware Trust Evaluaton The ratng about an nteracton between agents can be one of n dscrete levels (level,,leveln). Each level represents the extent to whch an agent s satsfed wth the nteracton, n whch level means extremely unsatsfed and leveln means extremely satsfed. Context nformaton of nteractons should also be consdered to mprove trust evaluaton accuracy. We consder m types of context nformaton, and Cj ( {, 2,, }) use m to represent the jth value of context type, then the context nformaton of an ( C, C,, C ) j nteracton can be represented as a tuple C 2 j 2 mj m. It s reasonable to assume that states of dfferent context types are ndependent. To sum up, a ratng conssts of a servce level nteger and a context tuple. We use a naïve-bayesan networ for trust evaluaton, where trust value s the root node and context nformaton corresponds to leaves. Thus structure s hghly extensble, when addng a new context type, the only thng to do s nsertng a leaf node and exstng condtonal probablty tables (CTs) are stll vald. Smlarly, removng a context type doesn t have any effect to left nodes ether. An example s shown n Fg., where trust s related to two types of contexts (Context_ and Context_2). Node Trust has fve states, from level to level5, correspondng to fve possble ratngs. Node Context_ has two states: context and context2, and node Context_2 s two states are context2 and context22 respectvely. The CTs (.e. ( Trust = level ), {,2,3,4,5} ( Context _ = context j Trust = level ), j {,2}, {,2,3,4,5 } ( Context _ 2 = context2 Trust = level ), j {,2}, {,2,3,4,5 }, j can be learned from the ratngs (cases). The tme decay process can be done after a perod of tme or after learnng some number of cases by fadng old probabltes before tang new ratngs nto consderaton. Equaton (4) shows the CT updatng process of node Trust, n whch, (m) (m 0) s the probablty after m fadng rounds; λ [0,] s the fadng factor. (0) ( Trust = level ) ( m+ ) ( Trust = level ) = n ( = m) ( Trust = level )( λ ) m + ( 2 m) λ + λ En λ = e [ 0,] We beleve that the fadng factor should reflect the stablty of servce enttes behavor, rather than tang fxed value as n most exstng trust models. In fact, the certanty degree of ratngs can represent the change of trust value, because the more the entty changes ts behavor, the more the dfference between the current ratng and the expectaton of trust value s,.e., the smaller the certanty degree s. So we set the fadng factor be the certanty degree of the last (newest) ratng that has been taen by the Bayesan networ as evdence. Gven the ratngs, the trust evaluaton process, that s the calculaton of numercal characterstcs (.e., Ex, En, He) and each ratng s certanty degree (.e., μt(r)), can be descrbed as Algorthm. Algorthm Basc context-aware trust evaluaton Input: The set of ratngs (cloud drops) R whch ncludes context nformaton Output: Trust cloud s three parameters Ex, En, He and each ratng s certanty degreeμ T (r) Step: Intalze all the CTs to be unform dstrbuton Step2: = repeat Read n a ratng r and related context nformaton tuple C from R If needed, do tme decay process as n (4) Use the ratng as a new case to update the CTs = + untl read n all ratngs ) (4)

5 456 Bo Jn et al. / roceda Envronmental Scences (20) Step3: Infer the probablty that an agent s servce qualty s on level n each dfferent context C (.e., ( Trust = level C), {, 2,, n}) Step4: Calculate Ex n each dfferent context C as the expectaton of node Trust n context C usng n Ex = ( Trust = level C) = Step5: Calculate En n each dfferent context C as n () Step6: Calculate He n each dfferent context C as n (2) Step7: for j = to - do Calculate μ T (r j ) n the related context C as n (3) end for Fgure. Bayesan networ for context dependent trust estmaton. 3.2.Unfar Ratng Flterng In order to avod unfar ratngs affectng the accuracy of trust evaluaton, trustors should frst evaluate raters relablty and then select the most relable ones. For each rater, ts relablty can be estmated by the average certanty degree (μt(r)) of all ratngs from t. The larger the average certanty degree, the more relable s the rater. For sae of computaton, we select at most two relable raters whose relablty s larger than some specfc value, say 0.6, and flter out all other ones. 3.3.Servce rovder Selecton Based on Trust Cloud We argue that uncertanty of trust should be taen nto consderaton for servce provder selecton. So, we use trust cloud as the decson crtera, that s to say, not only the trust value (whch s represented by Ex) but also ts uncertanty (whch can be represented by En+He accordng to () and (2)) s compared. Obvously, enttes wth hgher trust value and lower uncertanty are more trustworthy, whle those wth lower trust value and hgher uncertanty are not trustworthy. Varous servce entty selecton approaches can be desgned for specfc applcatons. In ths paper, we select the entty wth smaller En+He from the two enttes wth the most largest Ex.

6 Bo Jn et al. / roceda Envronmental Scences (20) Expermental analyss 4..Experment Settng There are 00 enttes, 60 of whch belong to group A and 40 belong to group B. Each entty s behavor s determned by a behavoral probablty tuple (p, p2, p3), whch s correspondng to servce level (level, level2, level3). We consder two context attrbutes and both have two states as descrbed n prevous secton, so there are four dfferent context stuatons. The orgnal (change later) probablty tuples for the two groups are shown n Table I. The smulaton s dvded nto 00 rounds and around 25 rounds for each stuaton. In each round, each entty selects a servce provder entty accordng to some crtera (see Secton B). In order to smulate the dynamc nature of entty behavor, the probablty tuples are changed randomly after every 0 rounds as n (6). p = p Δ, p2 = p2 +Δ, p3 = p3 Δ (wth probablty 0.33) 2 2 (6) p = p + Δ, p2 = p2 Δ, p3 = p3 + Δ (wth probablty 0.33) 2 2 p = p, p2 = p2, p3 = p3 (wth probablty 0.34) Δ= 0.0 So the enttes fade all the CTs each tme after learnng every 0 cases, and then ncorporate the latest cases. The fadng factor λ equals 0.0. The proporton of unfar raters s 40%. Table. Enttes orgnal behavor patterns Behavoral Group robablty Tuple A B Context, Context 2 (0.05,0.05,0.9) (0.9,0.05,0.05) Context, Context 22 (0.,0.,0.8) (0.8,0.,0.) Context 2, Context 2 (0.,0.2,0.7) (0.7,0.2,0.) Context 2, Context 22 (0.2,0.2,0.6) (0.6,0.2,0.2) 4.2.Experment Results We use successful servce rato to evaluate the accuracy of our trust model. If a selected servce provder behave good, that s ts servce level s level3, then the servce s successful, otherwse, t s fal. We compare the ratos n the followng three dfferent scenaros. Enttes don t use trust evaluaton, that s, they select servce provders randomly. Enttes use the trust evaluaton but don t consder context nformaton. Enttes use the trust evaluaton and consder context nformaton. Table II shows the results n four possble context stuatons. Table II Successful servce rato n dfferent scenaros Successful Trust Evaluaton Scenaros Servce Rato (%) Random Trust wthout context Trust wth context Context, Context

7 458 Bo Jn et al. / roceda Envronmental Scences (20) Successful Trust Evaluaton Scenaros Servce Rato (%) Random Trust wthout context Trust wth context Context, Context Context 2, Context 2 Context 2, Context We can see from Table II that our trust model wth can help mprove the successful servce ratos largely, from around 60% (Random) to hgher than 77% (Trust wthout context). In addton, context nformaton has good effect on estmaton accuracy, the ratos n the rght most column (Trust wthout context) are around 5% hgher than those n the mddle column (Trust wth context). The only excepton s n the last context stuaton (Context2, Context22), because entty behavor s more uncertan than n other stuatons, the rato wth context (79.%) s a lttle bt lower than that wthout context (80.8%). As enttes only select relable recommenders, t would be expected that the proporton of unfar recommenders has lttle effect on the accuracy of estmaton. The proporton of unfar recommenders n the experments shown by Table II s 40%. When the proporton of unfar recommenders s 70%, the smallest successful servce rato s as hgh as 75% (Trust wthout context). 5.Conclusons A trust evaluaton model whch combnes the cloud model and Bayesan networs s proposed n ths paper. The model has the followng features. The uncertanty of trust s explctly represented and evaluated usng sound theores for uncertan reasonng, whch mae the evaluaton more accurate even when enttes behavors change dynamcally. Context nformaton s explctly ntegrated nto trust evaluaton to mae the servce provder selecton be context-ware. Our future wors wll focus on unfar ratng flterng and trust aggregaton. Acnowledgment Ths paper s supported by the Openng roject of Key Lab of Informaton Networ Securty of Mnstry of ublc Securty (The Thrd Research Insttute of Mnstry of ublc Securty), Chna (Grant No. C0604); and by the Key rogram of Shangha Commttee of Scence and Technology, Chna (Grant No ); and by the Soft Scence Research rogram of Shangha Commttee of Scence and Technology, Chna (Grant No ).

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