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An indirect security trust method based on credibility evaluation.

1. Introduction

Direct trust can assess the credibility of each node based on the interactive experience with known nodes are part of known nodes. However, when a node is completely unknown, the reliability of it is calculated in accordance with recommendation received from other nodes. Reliance on indirect recommendations may also lead to wrong decisions with a dishonest recommendation. Recommendations can enhance the trust values of suspicious nodes, as well as reduce the trust values of honest nodes, which provide a dishonest recommendation. Thus, it is a fundamental question in the trust model, whether there is a mechanism which can avoid the impact of dishonest recommendations [5].

In the current trust model, recommendation service only provides a single trust value (obtained by the interaction with evaluating nodes). However, such single trust values represent the provider's subjective view of the unknown entity which cannot well reflect the trust level under certain circumstances. For example, S requests recommendation of unknown entity and receives recommendations provided by A and B, the recommended values [T.sub.A] = 5, [T.sub.B] = 2.5. Does this means the same weight should be given to both sides in the complex recommendation trust calculation? Assuming TA is obtained at a time [t.sub.A], and TB is obtained at a time [t.sub.B]. tcurrent-[t.sub.A] > tcurrent-[t.sub.B]. Compared with B, A has less interaction history with C. Obviously, not only does B has a recent interaction, but also B has more interactive experience. So when assessing the credibility of C, the recommendation provided by B should be more reliable than that by A. In addition, the credibility of B's recommendation should be strengthened, if the interaction between B and C is more sensible than that between A and C, in the context of the interaction [6].

To solve this problem, this paper presents an efficient method for calculating indirect trust. The premise of the model is that only honest recommendation can be referenced in the recommended trust calculation. Further, when calculating the complex recommendation trust value of an unknown entity, more weight should be given to honest reliable recommendations than unreliable ones. The effectiveness of this model has been proven in the experiment. In the framework of the proposed model, when an end-entity requests a kind of service, the service provider seeks recommendations from another peer service provider. All recommendations must go through the filtering mechanism "outlier detection engine". "Outlier detection engine" uses deviation detection proposed in [1] to determine whether the recommendation is dishonest, then uses the "recommended assessment model' to calculate credibility of every honest recommendation. This model uses the method of weighted average (credibility as weight) to calculate the complex recommendation trust value. The basic function of "policy analysis" is to process the request, deciding whether the end-entity that requests the service would be allowed to do request activities, according to the recommendation trust value after calculation and strategies provided for this service.

2. Outliers detection engine

In order to make other service providers provide recommendations, the original service provider Sq creates a "recommendation request message" ([R.sub.REQST]), and broadcasts the news to the neighbor nodes, trusted nodes and other unknown nodes. Then the original service provider Sq waits to receive recommendations. Recommender nodes that have had interaction with the target end-entity may answer [R.sub.REQST] message ([R.sub.RESP]) with the recommendation response message ([R.sub.RESP]). Recommendation providers feedback some attributes (these attributes define some historical interaction information between recommendation providers and assessed terminal) rather than just a single recommendation trust values to Sq.

Request and the corresponding message is defined as follows:

[R.sub.REQST] [Svc_ID, Entity_ID, Req_Time] [R.sub.RESP] [Recomm _ID, Entity _ID, Ti, t, [n.sub.t], SS]

In RREQST, Svc_ID represents the identity of service provider Sq that requests recommendations; Entity_ID represents the identity of target end-entity; Req_Time represents the time of recommendation request. In RRESP, Recomm_ID says the recommender's identity, Ti is the recommended value of recommender i, t is the time recommended value recorded, [n.sub.t] said the number of interactions that the recommendation is based on, SS is the sensitivity of the recommender.

The purpose of the indirect trust calculation is to determine the trustworthiness of a strange entity from a group of recommendations, thus narrowing the gap between the recommended trustworthiness and actual trustworthiness of the entity. In this framework, the "outlier detection engine" is responsible for detecting dishonest recommendations. "Outlier detection engine" defines a dishonest recommendation as an abnormal value, which appears inconsistent with other recommendations and does not meet the data distribution of other recommendations. In the field of database and data mining, this method has been fully aware of the importance of detecting outliers [2]. First proposes the method based on the deviation from the abnormal value: positioning the abnormal value by the method of dynamic programming. The model in this paper has extended the qutlier detection technique, so that he can filter dishonest recommendations. This method is based on the following fact: if a recommendation deviates from the median value of the given recommendations set, and the likelihood of this happening is low, then this recommendation is filtered as a dishonest recommendation. If an entity X requests for access to service A, A does not have enough interactive experience with X, then it will broadcast on the recommendation request from X. R is defined as collected recommendations set, wherein n is the total number of recommendations.

Because a smart attacker's recommendation may have little deviation, so as to not easily detected, this model divides all the possible recommended values into ten intervals [3]. For example, [Rc.sub.1] consists of recommendations recommended by the values between [0, 0.5]; [Rc.sub.2] consists of recommendations recommended by the values between [0.5, 1]; [Rc.sub.3],...[Rc.sub.10]; and so on. We think recommendations are similar if they fall within the same intervals. When all the recommendations have been assigned to the respective intervals, filtering engine computes a histogram, which indicates the recommendation number falls within each corresponding interval. H is defines as the histogram of a set of recommendation class [4].

H(R) = {<Rc1, f1>, <Rc2, f 2>, <Rc3, f 3>, <Rc4, f4>, <Rc5, f 5>, <Rc6, f6>, <Rc7, f 7>, <Rc8, f 8>, <Rc9, f 9>, <Rc10, f10>} Where, f is the frequency of recommendations that fall in the. From this histogram, remove all fi = 0 recommendation class, and get a domain set (Rdomain) and a frequency set f).

Rdomain = {Rc1, Rc2, Rc3, ..., Rc10}

f = f1, f2, f3, ..., f10}.

Definition 1: Define the dissimilarity function DF(xi) as:

DF([x.sub.i]) = [[absolute value of [x.sub.i]-median(x)].sup.2] / [f.sub.i] (1)

Where [x.sub.i] is a recommendation category of the recommendation x. In this method, DF value of [x.sub.i] is proportional to the absolute median deviation (Median Absolute Deviation, MAD, i.e., [[absolute value of [x.sub.i]-median(x)].sup.2]. MAD is used to detect the degree of deviation because of being sensitive to outliers. Abnormal value may significantly change the value of MAD. Further, DF value of [x.sub.i] and is inversely proportional to its frequency [f.sub.i]. So if a recommendation value deviates from other recommendation value, and its frequency is very low, then the corresponding dissimilarity DF of the recommendation is a great value. Similarly, if a recommendation value close to other recommendation values, and its frequency is very high, then the corresponding dissimilarity DF of the recommendation is a small value.

For each [Rc.sub.i], use the formula (1) to calculate its dissimilarity DF, i.e. the dissimilarity of other recommendation frequency. Put all recommendation classes of Rdomain in reverse order by dissimilarity DF ([Rc.sub.i]). DF ([x.sub.i]) maximum recommendation class is considered most likely to be dishonest recommendation, and it should be filtered out. Once Rdomain has sorted, the next step is to determine a set of dishonest recommendation classes.

Definition 2: Define the SF function of SRdomain as:

SF ([SRdomain.sub.j]) = C (Rdomain - SRdomainj) * (DF(Rdomain) - DF (Rdomain - [SRdomain.sub.j])

Where j = 1, 2, 3, m; m is the number of elements in SRdomain. C is the number of elements in (Rdomain--[SRdomain.sub.j]). DF(Rdomain) is obtained by adding the DF value of each element in Rdomain. SF shows how much the dissimilarity reduces after a group of suspected recommendations (SRdomain) have been removed from Rdomain.

Definition 3: If SF(SRdomainx) > SF(SRdomainj), j is any value of 1, 2, 3...,m-1, m except x, then SRdomainx is a group of dishonest recommendations of SRdomain.

To find a group of dishonest recommendation domain [Rdomain.sub.dishonest], this paper defines the following mechanisms:

(1) [Rc.sub.k] is the k-th recommendation class in Rdomain; SRdomain is one of the suspect's recommendation class. SRdomain [subset is equal to] Rdomain

(2) Initialize SRdomain = {}

(3) For each SRdomaink, calculate SF ([SRdomain.sub.k]). [SRdomain.sub.k] is obtained from merging [SRdomain.sub.k-1] and [Rc.sub.k]. That is [SRdomain.sub.k], = [SRdomain.sub.k-1]*" [Rc.sub.k], where k = 1, 2, 3 ..., m-1, m is that number of recommendation classes after sorting.

3. Credibility measuring

For each recommendation, there is a certain degree of credibility to determine whether the recommendation is credible. The credibility of a recommended is determined by the following three aspects: (1) Experience (E); (2) Time Base Experience (TBE) (3) Sensitivity of the Service (SS).

TBE representatives the time the recommender last had interaction with the target terminal. In calculating the credibility of recommendations, the introduction of TBE is to integrate constantly decay experience. Change not only means to adapt to new things, but also means that the old things gradually forgotten. Anthropological studies show that as time goes on, new things will gradually slow decay rate. Indirect trust calculation mechanism, based on an anthropological perspective, uses interactive historical data. Therefore, the interactive experience with the target terminal will inevitably affect real-time view of the target terminal, and the newer experience should account for a higher weight in decision-making. To calculate TBE, recommendation service extracts the last interaction time from each RRESP. Make t and [t.sub.c] respectively represent the last interaction time and the present time, the TBE of target end-entity should be calculated as

TBE = [alpha] [(1-[beta]).sup.[DELTA]t/[alpha]] (3)

Among them [DELTA]t = [t.sub.c] - t, [alpha] and [beta] are normal adjusted values that can be adjusted through the definition of recession rate. (One day is considered as a period of time).

E represents the total number of interaction the service provider has with a terminal entity in his life cycle. It measures the number of activities between the end-entity and the service provider. The more interactions a service provider has with the target end-entity, the more experience he gets. Thus, the more interactions with the destination terminal number are, the more credible the recommendation is. In this method, the recommendation service extracts interaction number [n.sub.t] from RRESP. Because credibility is the value within [0,1], we need a format function that is used to limit the number of interaction between [0,1]. The function we used to format the interactive value is as follows:

E = [n.sub.t]-[n.sup.min.sub.t] / [n.sup.max.sub.t]-[n.sup.min.sub.t] (4)

Wherein [n.sup.min.sub.t] and [n.sup.max.sub.t] espectively represent the minimum and the maximum number of interactions. [n.sup.min.sub.t] = 1, 1 [less than or equal to] [n.sup.max.sub.t] [less than or equal to] [infinity].

SS: Ubiquitous environment is conceived as a physical space wealthy of equipment and service and it is able to carry out interaction service with the user, the physical environment and the external network. We can classify these services depending on the type of various service provided by ubiquitous environments. For example, a simple scanning device has a lower sensitivity than the file service. More sensitive recommender has higher weight than less sensitive ones. The hidden meaning of idea is that a malicious service requester can have large number of interactions with low sensitive service to enhance his trust value. However, malicious intent can be effectively detected after using recommendation system that combines experience and sensitivity.

4. Credibility evaluation based on fuzzy reasoning

Because of the ambiguity, ambiguity and subjectivity of the measure of credibility, so it could not be measured as a discrete value. This method introduces a fuzzy inference engine for assessing the credibility of recommendations. Fuzzy reasoning is using fuzzy logic to develop a given input to output mapping process. This mapping provides a basis for decision or pattern recognition. In this method, E, TBE and SS are input as fuzzy inference engine. Output of fuzzy inference engine is the credibility assessment. Fuzzy reasoning process has three main steps: fuzzification, rule evaluation, and defuzzification, as shown in Figure 1.

(1) Fuzzy: using fuzzy control to convert the clear input to the fuzzy language value of corresponding membership function. Put E, TBE, SS into a vector, as X* = [x1* x2* x3*] = [E TBE SS].Wherein, X* represents a real clear input. Each input variables are divided into three categories of fuzzy sets: Low (L), Medium (M), High (H). X between each fuzzy set is characterized by a membership function (MF) and an associated x between [0, 1]. In the model, we define the input Gaussian membership function MF: [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII].




Where, d = {L, M, H}. [x.sup.d.sub.1], [x.sup.d.sub.2], [x.sup.d.sub.3] and [[sigma].sup.d.sub.1], [[sigma].sup.d.sub.2], [[sigma].sup.d.sub.3] are constant parameters, representing the mean and variance of input fuzzy set. In the model, using a Gaussian blur mapping x * [member of] R3 to fuzzy set X.


Where a1, a2, a3 are positive integer, and using [a.sub.1] = 2 max


Output variables consist of five fuzzy sets ([Cr.sup.g]): VL (Very Low), L, M, H and VH (Very High). The Gaussian MF for five fuzzy sets is defined as


Where g = {VL, L, M, H, VH}, [y.sup.-g] and [[rho].sup.g] are respectively the mean and variance of the output fuzzy set.

(2) Rule evaluation: Fuzzy reasoning is estimate about fuzzy relations, which is based on logic rules in the fuzzy rule. In this method, we choose to use the Product Inference Engine (PIE) to deal with the fuzzy output. Fuzzy rules library that is used to assess the credibility of recommendations is based on the following axioms:

Axiom 1: For highly sensitive services (SS), i.e., a large number of interactions with the target end-entity (E) and most of them are recently interaction (TBE), Cr is a high reliability.

Axiom 2: For low-sensitive services (SS), i.e., a small number of interactions with the target terminal (E) and most of them are over a long time (TBE), Cr is a low reliability.

Based on these axioms, this paper presents 27 kinds of fuzzy inference rules, based on three fuzzy input functions and a fuzzy output function, to measure credibility, and these 27 rules are in the following table 1. This paper uses PIE, as the formula (10) defines, based on fuzzy rules and linguistic to process fuzzy input. PIE structure, based on the individual rules of inference, consists of the Mamdani method, t-norm and the maximum operation [25] of s-norm. PIE is defined as


Wherein for a given model, the fuzzy rule is represented by l. l = 1, 2,...,27. Through decomposition and simplification, the above equation simplifies to:




(3) Defuzzification: This is the process of converting fuzzy output to exact output. Use the blur eliminator, a map output PIE to discrete points y*, y* represents the best point. Blur elimination by the method of gravity center specified y* by collating the area covered by the value of Cr. But because of the complexity of the calculations, bur elimination by the method of center average, proposed in the literature, can calculate the approximation of y*. The formula is as follows:


Wherein [y.sup.g] and [] respectively represent the center of either fuzzy sets, and [w.sup.g] is its height.

After calculating the credibility of each recommendation, the service provider Sq uses the credibility and trust values to aggregate compute the recommendation trust value ([T.sub.recom]) of the end-entity. As the weight the heavy trust value, each recommended credibility calculated indicates the influence degree of each recommendation in the polymerization process. Therefore, the higher the recommended reliability is, the greater weight it holds in the polymerization. The service provider uses each recommended credibility and recommendation to calculate [T.sub.recom], with the following formula:


Where I represents the number of recommended. According to formula (13), if the recommendation requester did not receive any recommendation information which equals to i = 0 then [T.sub.recom] = 0. This situation is likely to occur. For example, a service requestor is a new network node in the network, and not with any other service providers have interaction. Not only that, if the recommender's SS is very low, and only long ago has several interactive with entity. Then the credibility of recommendation provided by recommender may be 0.

Experiments and Analysis.

5. Experiment and Analysis

In order to explain the calculation process of establishing trust recommended value between the neighbor nodes (strangers' nodes), this part gives an example to explain. Suppose A is a service provider on the Internet, A uses this model to detect the dishonest recommendation and calculate an unknown entity recommendation trust value.

For the application of this model, some parameters to configure the model needs first. In the calculation in the recommendation credibility, the parameters which models need to use are in table 2.

In this scenario, assuming for the service provider X, A is an unknown node, and X wants to ask A for service. Because A and X no interaction before, in order to determine the X trusted values and access level, A will be prior to those with X had the same terminal interactive request recommended. Table 3 shows that A sends RREQST information and A receives feedback from the same level's terminal. The information will be handled by model. Recommendation trust value computation steps are as follows:

Through the "anomaly detection engine", we can know that, R2, R5, R8 were found to be dishonest recommendations.

When receiving a recommendation, it is passed to the qutlier detection engine. Outlier filtering engine uses a detection mechanism based on deviation to filter dishonest recommendations. Experiments and Analysis.

Step 1: After receiving a recommendation from the peer-to-peer service provider, the recommendations are grouped into different intervals according to the recommendation value Ti. Table 4 shows the RDomain formed after received recommendations have been assigned to different intervals.

Step 2: After dividing received recommendations into different categories, remove recommendation whose value is 0 and calculate the DF ([Rc.sub.i]) of each recommendation.

Step 3: Calculation of each RDomain's SF are derived from the sorted RDomain. Because the Rc10's deviation value is the maximum, it is the most suspicious recommendation. Add it to the suspicious recommendation domain (SRDomain1), then calculate the SF value of SRDomain1. Then, combined with SRDomain1 and a recommended Rc8 to form SRDomain2, and then calculate the SF. For each Rci in the Rdomain (i < m, m is the number of recommendation classes in Rdomain), the process is repeated. As shown in Table 6, SRdomain3 has the largest SF value. So the SRdomain3 recommendation {5, 4, 5} is considered to be a dishonest recommendation, and should be removed from the Rdomain.

5.1 Calculation of Credibility

If [R.sub.v] represents the recommendation set after removing dishonest recommendations. The steps below explain the process of calculating the recommendation trust value by the "recommendation evaluation engine".

Step 1: Because the credibility and the recommendation trust value are defined in the [0, 1], we need to define the number and time of interaction between [0, 1]. For each effective recommendation in the [R.sub.v], we use type (3), (4) to calculate TBE, E. Table 7 shows the E, TBE, SS after calculating the recommendations in [R.sub.v].

Step 2: For each honest recommendation, calculate its credibility by the use of fuzzy logic to the E, TBE, SS to. First we use three inputs (E, TBE, SS), followed by type (5), (6), (7) to determine the membership degree of their own through the Gauss MF. These inputs are thought to be limited to numbers between [0, 1].

Step 3: When the input is fuzzy, we can know membership degree of each input to meet the demand. There are a total of 27 rules in this model. As shown in Table 2, each of the rules are composed of three inputs, and each input is divided into L, M, H. Each rule uses logic operation AND (= prod) to calculate the fuzzy input. The calculation of R1 for ru1 is as follows:


Step 4: Because the model uses the Gauss fuzzifier, according to the formula (11), we can get:


Step 5: After rules in the formula grouped according to fuzzy output set (VL, L, M, H, VH), the maximum value can be chosen to represents the group.


Step 5: The output of the PIE is an aggregated fuzzy output set containing a group of output values. So in order to obtain a unique value, we must carry on the defuzzification. Such as using formula (12) to calculate the reliability of R1:

Cr = 0.103 * 0 + 0.884 * 0.25 + 0.309 * 0.5 + 0.256 * 0.75 + 0.0048 * 1 0.103 + 0.884 + 0.309 + 0.256 + 0.0048 = 0.367

Each recommendation in the [R.sub.v] are repeated using the recommendation evaluation process to calculate their own credibility. The credibility is the weight of each recommendation in the trust value assessment. Table 8 shows the credibility of each recommendation in [R.sub.v].

5.2 Calculation of recommendation credibility

Integrate each recommendation value (Ti) and each recommendation credibility in the collection [R.sub.v] according to equation (13), the recommendation trust value of the entity Trecom is calculated as:

[T.sub.recom] = 0.367 * 1.15 + 0.475 * 0.95 + 0.54 * 1.4 + 0.805 * 0.55 + 0.291 * 0.45 + 0.613 * 1.3* + 0.448 * 0.7 / 0.367 + 0.475 + 0.54 + 0.805 + 0.291 + 0.613 + 0.448 = 0.935

5.1 Verification of abnormal value detection engine

In order to verify the affectivity of abnormal value detection engine, this paper compares the Quartile [33], Control Limit Chart [31], Iterative Filtering [34] and other detection methods1 and this method to verify their performance in dishonest recommendation detection. This paper applies these methods to detect dishonest recommendation in two different scenarios. In the first experiment, assuming that there is a certain proportion of dishonest recommendation. They give the recommended value is between 0.5-1.5, they use bad mouthing attack, the experimental results in figure 2. The second set of experiments, the recommender hypothesis is not honest to highly recommended value (4-5), they use Ballot Stuffing attack, the experimental results in Figure 3. In the two experiments, dishonest recommenders' ratio is between 10%-45%. In order to compare, this paper uses the Mathews correlation coefficient (Mathews Correlation Coefficient (MCC)) [35] measurement for the detection of four dishonest recommendations. The calculating formula of type MCC:

MCC = (TP * TN) - (FP * FN) / [square root of ((TP + FP) (TP + FN) (TN + FP) (TN + FN))]

Where TP is the number of really high reputation values, TN is the number of really low reputation value, FP is the number of false high reputation values, FN is the number of false low reputation value. MCC returns the -1 to a value of 1, 1 represents perfect filter, 0 representatives of stochastic filtering, -1 representatives were not filtered. In order to avoid infinite results in the calculation of MCC, so that if the denominator of four TP, FP, TN and FN and has a value of zero, the denominator is set to 1. When the dishonest recommenders increased from 10% to 45% changes, compared four methods of MCC as shown in Figure 3 and figure 4. According to the results, the method can effectively detect the dishonest recommendation.

5.2 Verification of recommendation value

To demonstrate the validity of the methods, this part simulates the experiment. The simulation is to verify whether the target entity credibility method to determine the effective computing trust value based on fuzzy inference. Because of the need to compare, methods are divided into four types as shown in table 9. In the experiment, we simulate the multi terminal environment, each terminal to provide or request recommended and continuous leave or join in the network environment. Accordingly, the terminal can be divided into two categories: recommendation requester (Recommendation Requestor Terminal, RRT) and recommended provider (Recommendation Provider Terminal, RPT). Each test, the new RRT requests to other RPT network recommendation to the unknown RPT, repeated the process. All RPT are likely to become a presenter terminal of other RPT (Recommendation Terminal, RT).

When the RRT send request to RPT, RT will take on recommendation. Recommendation values are between [0, 5]. According to the recommendation credibility to judge whether the RT is honest. Honest RA according to his own experience true provide recommended, however, dishonest RT according to his malicious intent to disguise his experience, provide a high, low or unstable recommended. Assume that each of RRT and RT in RREAST and RRESP format to exchange recommended. In order to define the recommendation credibility, random for each recommended distribution type: V L [0 1], L [1 2], M [2 3], H [3 4] and V H [4 5].

Test define a group of honest recommendation N_honest_RT and a group of dishonest recommendation N_dishonest_RT. The simulation is divided into N_ST E PS steps, each step, the percentage of dishonest_RT increased gradually. Each step, RRT radio and RREAST, then randomly select a group of RT. After each simulation run, recommendation trust RRT to table four methods and 9 respectively in the calculation of the value of SRA.

Validity of the method proposed in this paper for the analysis of the simulation environment, the definition, and this paper introduced two attack scenarios for recommendation method (bad mouthing and Ballot Stuffing).

5.2.1 Attack scenario 1

The attack scenario is dishonest recommender use bad mouthing attack on RRT, in order to reduce the reliability of RPT. The scene simulation variables as shown in table 10. In this scenario, a RRT collection RA recommendation about an unknown RPT. The real trust hypothesis RPT value is 4. Simulation of the initial, 10% in the environment are not honest RA, by giving them the recommendation trust low value (between [0, 1.5]), bad mouthing attack on RPT. In the seven step of each step, the percentage of RA increased 5% dishonest. Table 9 Comparison of recommend trust through the categories in the calculated values as shown in figure 5.

5.2.2 Attack scenario 2

The attack scenario is recommended dishonest Ballot Stuffing attack on RRT, in order to improve the reliability of RPT. The scene simulation variables as shown in table 10. In this scenario, the simulation is divided into seven step operation. Each step, a dishonest RA recommended by the high trust value (between [3, 5]) way launched Ballot Stuffing attack. The actual trust assumption RPT the value 0.75. Table 9 Comparison of recommend trust through the categories in the calculated values as shown in figure 6.

Two attack scenarios show that the method proposed in this paper to deal with some attack is more effective than the other methods. The recommendation method based on average value, the received recommended, do the calculated average value, get the Trecom, and then, with the increase of dishonest recommendation, Trecom also tend to be dishonest recommendation. In Figure 5, you can see, the effect of bad mouthing attack, recommendation trust value calculated trust value smaller than the actual. The recommendation method based on credibility, the credibility of precision affects the reliability and credibility of the recommendation. But in Figure 5, 6 shows, this method cannot accurately judge the entity credibility, because he cannot detect the entity of bifurcation behavior has a very high reputation. But this method can use technology to filter the dishonest recommendation, to obtain accurate results. Because, an honest recommendation, he may be obtained according to the long ago or inadequate experience, so these methods cannot be measured by an honest recommendation reliability. The proposed method can detect the dishonest recommendation, and consider the measure of each honest recommendation trust, recommendation trust so as to calculate the optimal value.

In summary, indirect trust recommendation based on computing plays a very important role in the comprehensive trust method. Calculation method for the indirect trust can not only distinguish dishonest recommendation, but also can calculate each credible honest recommendation. The method of the recommendation credibility measure concept can determine the influence degree of each recommendation. Credibility is measured by introducing the recommended three new parameters to calculate the corresponding information, these three parameters are E, TBE, and SS. Reliability is calculated by fuzzy inference engine completed.

6. Conclusion

This paper presents a method to calculate the indirect trust based on fuzzy inference. Due to the single trust recommendation to provide value represents the subjective view of his unknown entity, cannot well reflect the trust level in certain circumstances. The method can solve this problem. In the method, for each recommendation, have a certain degree of confidence, to decide whether the recommendation is credible. The recommendation credibility by interactive number, the time of the last update trust value sensitivity, provide recommendation service of the three decisions. This design method of an "anomaly detection engine", the engine is responsible for detecting dishonest recommendation. The experimental results can be well proved the validity of the method.

Received: 11 November 2013, Revised 13 February 2014, Accepted 26 February 2014

7. Acknowledgments

This work has been funded by College Natural Science Foundation of Jiangsu Province (11KJB520002), Jiangsu 973 Scientific Project (BK2011023, BK2011022), the National Natural Science Foundation of China (61272419, 60903027), China postdoctoral Foundation (2012M52108 9), Jiangsu Postdoctoral Foundation (1201044C), Jiangsu Natural Science Foundation (BK2011370), Research Union Innovation Fund of Jiangsu Province (BY2012022).


[1] Ramchurn, S D., Jennings, N R., Sierra, C., et al. (2004). Devising A Trust Model for Multi-agent Interactions Using Confidence and Reputation. Applied Artificial Intelligence, 18 (9-10) 833-852.

[2] Arning, A., Agrawal, R., Raghavan, P. (1996). A Linear Method for Deviation Detection in Large Databases. KDD, 164-169.

[3] Qianmu, Li., Hong, Zhang. (2012). Information Security Risk Assessment Technology of Cyberspace: a Review. Information--an International Interdisciplinary Journal, 15 (II) 4677-4684.

[4] Qianmu, Li. (2011). Multiple QoS Constraints Finding Paths Algorithm in TMN. Information- an International Interdisciplinary Journal, 14 (3) 731-738.

[5] Qianmu, Li., Jia, Li. (2012). Rough Outlier Detection Based Security Risk Analysis Methodology. China Communications, 5 (7) 14-21.

[6] Sugier, J. (2013). Computational Methods for Adaptation of Markov Models to Requested Maintenance Policies. Computer Modelling and New Technologies, 17 (1) 14-24.

Wei Xianghe (1),(2), Zhang Hong (1), Liu Zhen (1), Li Qianmu (1), Xia Bin (1)

(1) School of Computer Science and Technology Nanjing University of Science and Engineering Nanjing 210094, China

(2) School of Computer Science and Technology Huaiyin Normal University Huaian 223300, China

Table 1. Fuzzy inference rules of Cr

Rule name     Rule description

Ru(1)         IF E is L AND TBE is L AND S is L THEN Cr is VL
Ru(2)         IF E is M AND TBE is L AND S is L THEN Cr is L
Ru(3)         IF E is L AND TBE is M AND S is L THEN Cr is L
Ru(4)         IF E is L AND TBE is L AND S is M THEN Cr is L
Ru(5)         IF E is M AND TBE is M AND S is L THEN Cr is L
Ru(6)         IF E is L AND TBE is M AND S is M THEN Cr is L
Ru(7)         IF E is M AND TBE is M AND S is L THEN Cr is L
Ru(8)         IF E is H AND TBE is L AND S is L THEN Cr is L
Ru(9)         IF E is L AND TBE is H AND S is L THEN Cr is L
Ru(10)        IF E is L AND TBE is L AND S is H THEN Cr is M
Ru(11)        IF E is L AND TBE is M AND S is H THEN Cr is M
Ru(12)        IF E is L AND TBE is H AND S is M THEN Cr is M
Ru(13)        IF E is M AND TBE is L AND S is H THEN Cr is M
Ru(14)        IF E is M AND TBE is H AND S is L THEN Cr is M
Ru(15)        IF E is H AND TBE is L AND S is M THEN Cr is M
Ru(16)        IF E is H AND TBE is M AND S is L THEN Cr is M
Ru(17)        IF E is M AND TBE is M AND S is M THEN Cr is H
Ru(18)        IF E is H AND TBE is H AND S is L THEN Cr is H
Ru(19)        IF E is L AND TBE is H AND S is H THEN Cr is H
Ru(20)        IF E is H AND TBE is L AND S is H THEN Cr is H
Ru(21)        IF E is H AND TBE is M AND S is M THEN Cr is H
Ru(22)        IF E is M AND TBE is M AND S is H THEN Cr is H
Ru(23)        IF E is M AND TBE is H AND S is M THEN Cr is H
Ru(24)        IF E is M AND TBE is H AND S is H THEN Cr is H
Ru(25)        IF E is H AND TBE is H AND S is M THEN Cr is H
Ru(26)        IF E is H AND TBE is M AND S is H THEN Cr is H
Ru(27)        IF E is H AND TBE is H AND S is H THEN Cr is VH

Table 2. Indirect trust initial parameters

Parameters                              Value

Present Time tc                     "30 Nov 2012,

The minimum number of
interaction nmin                          1

The maximum number of
interaction nmax                          50

[alpha]                                  1.1

[beta]                                   0.1

Table 3. Recommend and recommended
the corresponding instance

Recommendation request information:

RREQST [SvcI D : 1023, Enti tyI D : 8745, ReqTime :
"Nov302013, 10 : 20 : 30 : 45"]

Corresponding recommendation information:

R1        RRESP[RecommI D : 1067, Enti tyI D : 8745, 1.15,
          "Nov 20 2013, 10 : 20 : 30 : 47", 30, 0.11]

R2        RRESP[RecommI D : 1238, Enti tyI D : 8745, 35,
          "Nov25 2013, 12 : 45 : 21 : 41", 45, 0.7]

R3        RRESP[RecommI D : 2154, Enti tyI D : 8745, 0.95,
          "Nov 19 2013, 08 : 16 : 38 : 15", 17, 0.6]

R4        RRESP[RecommI D : 3152, Enti tyI D : 8745, 1.4,
          "Oct 30 2013, 18 : 40 : 39 : 13", 37, 0.8]

R5        RRESP[RecommI D : 1863, Enti tyI D : 8745, 9,
          "Nov 17 2013, 13 : 29 : 12 : 55", 45, 0.9]

R6        RRESP[RecommI D : 2169, Enti tyI D : 8745, 0.55,
          "Nov 27 2013, 21 : 58 : 30 : 27", 45, 0.9]

R7        RRESP[RecommI D : 2041, Enti tyI D : 8745, 0.45,
          "Nov 06 2013, 11 : 34 : 04 : 42", 21, 0.2]

R8        RRESP[RecommI D : 1009, Enti tyI D : 8745, 4.75,
          "Oct 20 2013, 15 : 20 : 59 : 08", 36, 0.4]

R9        RRESP[RecommI D : 7130, Enti tyI D : 8745,1.3,
          "Nov 26 2013, 06 : 33 : 09 : 45", 29, 0.5]

R10       RRESP[RecommI D : 5308, Enti tyI D : 8745, 0.7,
          "Nov 09 2013, 19 : 45 : 01 : 31", 31, 0.7]

Table 4. Recommendation partition

Rci     Rec Value rci   Frequency fi

Rc1          0.5             1
Rc2           1              3
Rc3          1.5             3
Rc4           2              0
Rc5          2.5             0
Rc6           3              0
Rc7           5              1
Rc8           4              1
Rc9          4.5             0
Rc10          4              1

Table 5. Recommendation class order

Rci       Rec Value rci   Frequency fi   DF (Rci)

Rc10            5              1          12.25
Rc8             4              1           6.25
Rc7             5              1            4
Rc1            0.5             1            1
Rc2             1              3          0.083
Rc3            1.5             3            0

Table 6. The calculation results of SF

SRdomain          Rdomain-SRdomain   DF (Rdomain-SRdomain)     SF

{5}               {4,5,0.5,1,1.5}           11.333           110.25
{5,4}             {5,0.5,1,1.5}              6.583            136
{5,4,5}           {0.5,1,1.5}                0.333           162.75
{5,4,5,0.5}       {1,1.5}                    0.083            141
{5,4,5,0.5,1}     {1.5}                        0             70.749

Table 7. The E, TBE, after recommendation calculation in [R.sub.v]

Ri      nt                  ti                   E      TBE     SS

R1      30    Nov20 2012, 10:20: 30 : 45        0.6    0.422   0.11
R3      17    Nov 19 2012, 08:16: 38 : 15       0.34   0.383   0.6
R4      37    Oct 30 2012, 18 : 40 : 39 : 13    0.74   0.062   0.8
R6      45    Nov27 2012,21 :58: 30 : 27        0.9    0.825   0.9
R7      21    Nov 06 2012, 11 : 34 : 04 : 42    0.42   0.11    0.2
R9      29    Nov26 2012, 06:33: 09 : 45        0.58   0.749   0.5
R10     31    Nov 09 2012, 19:45: 01 : 31       0.62   0.147   0.7

Table 8. Each R recommendation credibility

Ri          E        TBE      SS       Cr

R1         0.6      0.422    0.11    0.367
R3         0.34     0.383     0.6    0.475
R4         0.74     0.062     0.8     0.54
R6         0.9      0.825     0.9    0.805
R7         0.42     0.11      0.2    0.291
R9         0.58     0.749     0.5    0.613
R10        0.62     0.147     0.7    0.448

Table 9. The classification of recommended method

Simulation                     Identifier    scenario 1   Scenario 2

The simulation run times       N_STEPS           7            7

The number of                  N_RT             500          500
recommender's RT

The percentage of honest       N_honest_         90           90
recommender RT                 RT(%)

Honest recommendation          Honest_         [3, 5]      [0, 1.5]
value range                    range

The percentage of              N_Dishonest       10           10
dishonest recommendation RT    _RT(%)

The percentage of dishonest    Dishonest      [0, 1.5]      [3, 5]
recommendation RT              _range

Dishonest each simulation      N_Dishonest       5            5
RT percentage increase         _RT_inc

The evaluation of RPT          TRPT              4           0.75
real trust value

Each collection of             Nrecomm          100          100
recommended number VH

The percentage of VH           CR_VH             20           20
recommendation H

The percentage of H            CR_H              20           20
recommendation H

The percentage of M            CR_M              20           20
recommendation H

The percentage of L            CR_L              20           20
recommendation H

The percentage of VL           CR_VL             20           20
recommendation H

Table 9. The classification of recommended method

Type                        Dishonest & detection     Evaluation of

Average ([26]                        NA                    NA

Reputation based                     NA              The reputation
model ([28] [29] [30])                               of recommender

Malicious                   Control Limit Charts,          NA
recommendation               Quartile, Iterative
filtering ([31]-[34])             Filtering

This method                The filtering mechanism   The credibility
                             based on deviation      of recommender
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Author:Xianghe, Wei; Hong, Zhang; Zhen, Liu; Qianmu, Li; Bin, Xia
Publication:Journal of Digital Information Management
Article Type:Report
Date:Apr 1, 2014
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