Bayesian Network Based Trust Model with Time Window for Pure P2P Computing Systems

Transcription

1 Bayesian Network Based Trust Model with Time Window for Pure P2P Computing Systems Jigyasu Dubey Department of Information Technology Shri Vaishnav Institute of Technology & Science Indore, India Dr.(Mrs.) Vrinda Tokekar Department of Information Technology IET, Devi Ahilya Vishwavidyalaya Indore, India Abstract In recent years P2P computing systems gain popularity and impact due to the anonymity, dynamism, scalability, and cost sharing. Due to autonomy of peers and open environment, security is big issue in P2P computing systems. Traditional security mechanisms used in centralized systems are not suited for P2P computing systems because there is no central point of authority. The working of a P2P computing system is highly dependent on collaboration of peers. To improve the collaboration between peers many trust management systems have been developed recently. This study proposed a Bayesian network based trust model with time window to measure the trustworthiness of task processor peer in pure P2P computing framework. The proposed model is compared with Bayesian network based trust model using JADE This shows that proposed model identify malicious peers earlier than the Bayesian network based trust model in a P2P computing system. Keywords P2P; Trust; Bayesian Network; Security I. INTRODUCTION In recent years P2P computing systems gain popularity and impact due to the anonymity, dynamism, scalability, and cost sharing. The P2P architecture provides an open, unrestricted environment for resource sharing [1]. It has ability to aggregate the resources like CPU cycles, memory, etc. of peers in order to build a system with enormous resources [2] [3]. However, specific characteristics of P2P architecture like heterogeneity, anonymity, autonomy, and openness makes it an ideal environment for attackers, and leads to many security problems in P2P computing systems. The P2P computing systems do not have any central point of control or authority. They have more security threats than centralized systems due to its open nature [4]. In a distributed system without centralized server for authority, providing security mechanism is more complicated. In pure P2P computing systems security issues are one of the major challenges that need to be taken care off. The key for correct functioning of the P2P System is collaboration between the peers. The P2P computing system expects certain level of honesty in peers. However as number of peer increases in a system, number of peer with malicious behavior also increases who are responsible for security attacks and threats to the whole system [3]. Due to decentralization in P2P systems, it is not possible to have any centralized mechanism to protect peers from threats and malicious peers. Peers are required to protect themselves, to be responsible for their own behavior and to inform other peers about threats and malicious peers in the system. At the same time, by some mean they also need to evaluate the facts received from other peers for their trueness and peers for their reliability. In P2P computing systems security issues relate more with trustworthiness. To encourage the peers for positive participation in P2P system, there is need for mechanisms to maintain the trust between peers in P2P computing systems. To encourage honest and co-operative behavior among peers, trust management is a successful approach to maintain trustworthiness of the system. In a system, trust between peers developed with time and is measurable [5]. To establish the trust between peers, reputation mechanism is one way which is also used as risk management technique. Reputation systems provide a way for peers to be able to trust other peers. The main issues in reputation system are management of trust measures, handling unfair feedbacks, controlling malicious attacks on peers and monitoring of free riders, those who are always interested in using system resources but do not contribute much to the system. In a P2P system, the trust of a peer in another peer depends upon the multiple attributes shown by that peer like- reliability, availability, honesty, truthfulness, security, competence, and timelines which depends on the environment and requirement of peer. Depending on circumstances peer s requirement are different, a peer require considering its trust in a specific attribute or in multiple attributes. The Bayesian networks provide a flexible method to present differentiated trust and combine different aspects of trust [6]. This study proposed a Bayesian network based trust model with time window to calculate trust between Task Distributor peers and Task Processor peers in pure P2P computing frame work [7]. This study modified the Bayesian network based trust model [6] by adding the aspect of time window in calculation of trust. II. RELATED WORK The research on trust and reputation has attracted a lot of interest from researchers in P2P networks. The P2P networks are open, distributed and composed of autonomous peers that

2 act on behalf of their users. Hence the key to a successful P2P system is cooperation among the peers of that system while achieving their individual goals [8]. In last few years many trust management techniques have been developed for P2P computing networks. Here we briefly describe some techniques developed for providing security in P2P computing systems. Xue Chen, Guisheng Chen and et al. in [4] summarized the problems for trust management in different kind of P2P networks. The authors classify peers in three categories according to their contribution in the network: good nodes, malicious nodes, and average nodes. The good nodes share the authentic files and try to offer service to other peers. Malicious nodes are attacker of the system. The average nodes are free riders. They also give trust classification and identify some trust factors for building a trust model in P2P networks. For trust evaluation, authors introduce the general trust function. However, focusing on specific application like distributed computing, there may have specific trust factors that should be considered besides the general trust factors given by the authors. Stefan Kraxberger and Udo Payer in [9] specify a P2P model and gives security concept for this. In this work authors divide security concept in to two parts: routing security concept and group security concept. Ajay Ravichandran and Jongpil Yoon in [10] proposed a trust management algorithm called Eigen group trust. The algorithm built on top of a peer group infrastructure that not only manages trust within groups but also between different groups. In this work authors proposed a hybrid solution connecting reputation management and peer communities. The proposed algorithm is based on Eigen Trust where a peer will calculate global trust value by obtaining reputation values from all peers in the network. They also propose a delegation model that is extended to any group based P2P community and also provide an effective credibility measure. However, in this work authors do not considered anonymity of peers. Jung-Tae Kim, Hae-Kyeong et.al. in [11] brief about different issues related to security in P2P systems including identified threats, various protection methods used and give suggestions for further enhancements on the security mechanism in P2P systems. Chen Ding, Chen Yueguo et.al. in their work [4] present a computational trust management model to shows the relationships based on trust and reputation by calculating reputation scores. The authors also studied DMRep and EigenRep reputation based trust management protocols, through which the computation and maintenance of reputation scores are analyzed. However more extensive evaluation methods over wider parameters are required. Alvaro Arenas, Michael Wilson et.al [5] present an overview of the different concepts and technologies relevant to trust management in Grid systems. They also give the relation between trust and security in distributed system and Grids. Tu Chao et al. in [12] presented an improved trust model which not only considers a value of reputation of service to measure the quality of service, but also adds credibility of recommendation to measure the accuracy of the information of recommendation. The authors in same introduce the random extraction algorithm based on credibility of recommendation to select the recommendation peers, which reduces traffic and also enhances credibility of information of recommendation. Yao Wang and Julita Vassileva in [6] present a Bayesian network-based trust model for Peer-to-Peer file sharing applications. This model describes a trust and reputation mechanism that allows agents to identify companions who meet their individual requirements, through individual experience and sharing experiences with other agents with similar preferences. However some more aspects need to be added in the Bayesian networks that influence the system performance. The aspects used in Bayesian network are related to the only P2P file sharing networks. The aspects which influence performance of P2P computing systems need to be identified and these different aspects may be used to develop Bayesian Network based trust model for P2P computing systems. III. BAYESIAN NETWORK BASED TRUST MODEL WITH TIME WINDOW In a P2P computing system, task processor peer s capabilities are not uniform. For example, some task processor peers may be connected through a high speed network, while others connect through slow modem. Some task processor peers may have high speed processor while others have slow processor. Therefore task processor peer s capability can be represented by various characteristics, such as peer credibility, peer computation time [13] and peer distance [14]. A. Peer Credibility It is the probability that the result produced by a task processor peer is correct and is denoted by C P. It defines as: (1) Here E R, C R, and I R represent the number of erroneous results, correct results, and incomplete results respectively. The denominator E R + C R + I R represents the total number of tasks that a task processor peer computes. B. Peer Computation Time It is the expected computation time when a peer processes the system s computation during IT denoted by PC T. (2) Where IT is peers ideal time and A P is peer availability. The PC T represent the time when a peer actually executes the system s computation in the presence of peer autonomy failures. C. Peer Distance It is the distance between the task distributor and task processor peer and denoted by D (T D, T P ), where T D is the task distributor peer and T P is a task processor peer. It is defined as the time interval between the instance where T D dispatch a task unit to T P, and the time the result of this task unit is returned to T D. The distance D (T D, T P ) is the turnaround time of a task unit, and given as: (3)

3 Where T S denotes the time when T D dispatches a task unit to T P, and T C denotes the time when T D receives result of same task unit. D. Bayesian Trust In a P2P computing system the task distributor peer want to know the task processor peer s overall capability. Here we apply Bayesian network-based trust model proposed by Y. Wang [6] to represent the probability relationships between the overall evaluation and different characteristics of a task processor peer in pure P2P computing system as shown in Fig.1. The root node R represents a task distributor peer s evaluation on transactions with a task processor peer. The leaf nodes represent different characteristics of the task processor peers. Each node is associated with some value as shown in Fig. 1. One can easily obtain different probabilities of R given various conditions. The different probabilities of R represent the different trust values under the different needs of task distributor peer. Here task distributor peer can develop his Bayesian network based on own previous transactions with task processor peer, which provides an automatic way to adapt to its needs. A naïve Bayesian network provides a flexible way to represent the trust between a task distributor peer and a task processor peer. The task distributor peer develops a naïve Bayesian network for each task processor peer that it has interacted with. Every Bayesian network has two values for the root node R satisfied and unsatisfied, denoted by 1 and 0, respectively as shown in Fig. 1. The p(r=1) represent value of task distributor peer s overall trust in the task processor peer s capability to process a task. The p(r=1) is percentage of transactions that are satisfying and p(r=0) is percentage of not satisfying transactions occur within a time interval K'. E. Time Window Time Window of length K consists of the most recent K transaction, task distributor peer T D undertook with task processor peers in the transaction table. If the upper bound and lower bound of the time window is α and β respectively, we represent time window K as α β + 1. The time window will be personalized according to the requirements of the task distributor peer. TABLE I. Outcome THE CONDITIONAL PROBABILITY TABLE FOR NODE T The Number of Transactions given the outcome in time window K R=1 A p (R=1) = a / (a + b) R=0 B p (R=0) = b / (a + b) TABLE II. High Medium Low THE CONDITIONAL PROBABILITY TABLE (CPT) FOR NODE PEER CREDIBILITY The no. of transaction given R=1, in Time Window K m1 m2 m3 when R=1 = high R = 1) = m1 / a = medium R = 1) = m2/a = low R = 1) = m3/a The no. of transaction given R=0, in Time Window K n1 n2 n3 when R=0 = high R = 0) = n1 / b = medium R = 0) = n2 / b = low R = 0) = n3 / b Table 1 shows conditional probability table (CPT) for the node R. The a and b represent total number of satisfying transactions and unsatisfying transactions occur within time window K, respectively. It also stores the value of total transaction number when R=1 and R=0. The leaf nodes under the root node represent the different characteristics of the task processor peers. For every leaf node a conditional probability table (CPT) is defined. The node denoted by peer credibility, represents how much results produced by the peer are correct. It includes three values, high, medium, and low. Its CPT is shown in table II. Each column follows one constraint, which corresponds to one value of the root node. The sum of values of each column is equal to 1. Here m1, m2, and m3 represent the numbers of transactions satisfied when value for peer credibility are high, medium, and low respectively. Similarly n1, n2, and n3 denote the numbers of transactions unsatisfied with these three values. For example, p (credibility = high R = 1) is the conditional probability with the condition that a transaction is satisfied when credibility value is high. It measures the probability that task processor peer has high credibility and transaction is satisfied. The derivation to calculate this probability is given as below:- Fig. 1. Bayesian Trust

4 (4) The other probabilities in table II are calculated in similar way. With the naïve Bayesian networks, task distributor peer can set various conditions according to his needs and calculate its trust in a task processor peer in the various aspects from the corresponding probabilities. F. Updating the Bayesian Network A task distributor peer s trust in a task processor peer is built over time. After each transaction, the task distributor peer will update its corresponding Bayesian networks for the task processor peer to add its new experience. The trust model should motivate a peer to contribute continuously and thus including the effect of peer dynamics. It is unrealistic to consider peer s behavior from beginning of its first interaction or even to assign partial weights to transactions further down the time line. In our model, to motivate peers we consider peers present behavior rather than past behavior by providing time window. The time window represents the no. of last k transactions rather than all previous transactions. By defining a time window we ensure that the previous behavior of peer does not have an effect on its trust value, since trust should always be a measure of peer s current behavior. After each transaction with task processor peer, the task distributor peer will evaluate the transaction. The task distributor peer might have different criteria to judge a transaction. The overall evaluation of a transaction is a combination of evaluations of each aspect related to transaction, such as credibility, computation time, and peer distance. How to combine evaluations of each aspect depends on task distributor peer s requirements. For example some tasks require high accuracy while some concerned about fast processing. The result of overall evaluation, transaction is satisfied or not satisfied, is used to update the task distributor peer s trust in task processor peer. (5) Where W cr + W ct + W D = 1. Where W cr, W ct, and W D denote weights, indicate importance of respective aspects for task distributor peer. The task distributor peer has a satisfaction threshold S T. If S < S T, the transaction is unsatisfied. If S S T, transaction is satisfied. IV. THE METHOD In pure P2P computing system when a task distributor peer announces job availability, it gets the response from number of task processor peers to process task. If task distributor peer distribute subtask to such task processor peers which has low credibility or high distance, the task distributor peer will not get the correct results or will not get results within required time period. If this type of circumstances occurs repeatedly, the task distributor peer will be irritated. To solve this issue we use Bayesian network based trust model. When a task distributor peer receives responses from interested task processing peers, it makes the list of task processing peers according to their trust values. Now task distributor peer select the highly trusted task processor peers and distribute the task among them. If the task distributor peer has no previous transaction with the task processor peer, it query to other task distributor peers in the group. V. PERFORMANCE EVALUATION To evaluate the proposed method, a model to simulate the behaviour of task distributor peers and task processor peers has been developed in JADE The aim of this experiment is to see that the proposed method helps task distributor peers to select best task processor peers that match their preferences. In this experiment task distributor peer is interested in correct and fast result, i. e. those peers which have high credibility with fast computation time. For every task processor peer, it calculates the p as per (4) with time window k = 03. The simulation considers total 05 task processor peers named as TP1, TP2,., TP5 and 15 transactions T1, T2,, T15. Here peer TP3 and TP5 performs malicious behavior for some time. The same simulation is also run for the Bayesian Network based trust model and results of both models are compared as shown in fig 2 (a) & (b). The comparison, given in fig 3(a) & (b), shows that with time window equal to 03 the trust value of a peer fall 65% more than the Bayesian network based trust model after first malicious transaction, and it is almost 150% more in next successive malicious transaction. This shows that the trust value of malicious peers reduced very slowly in Bayesian trust model, however in proposed model the trust value of malicious peers reduced very fast. Hence this model identifies malicious peers earlier then Bayesian trust model in a P2P computing system. VI. CONCLUSIONS This study proposed a Bayesian network based trust model with time window to model differentiated trust and combine different aspect of trust in a Pure P2P Computing System. By the help of this model task distributor peer is able to identify good task processor peers to distribute task for processing. More specifically, the trust model allows task distributor peer to model the trustworthiness of task processor peers based on their previous transactions occur within time window. The proposed model is compared with Bayesian network based trust model. The comparison shows that proposed model identifies malicious peers earlier than Bayesian trust model in a P2P computing system. In this method every task distributor peer needs to maintain the Bayesian network for each task processor peer and CPT of each aspect. In a small size network this method is well suited but for large networks it has limitations because task distributor peers have their own limit to store number of Bayesian Network for every task processor peer. This method is useful in systems where task distributor peer repeatedly interact with

5 same task processor peers. This method is also applicable in large P2P networks because they have small world phenomenon hence number of Bayesian network required to maintain on task distributor peer are limited. (b) Fig. 2 (a) Bayesian Network based Trust Model & (b) Bayesian Network based Trust Model with Time Window. (a) (a) REFERENCES [1] Dejan S. Milojicic, Vana Kalogeraki, Rajan Lukose, Kiran Nagraja, Jim Prune, Bruno Richard, Sami Rollins, Zhichen Xu, P2P Computing, HP Lab Technical Report, HPL R1, March [2] INTEL, P2P-Enabled Distributed Computing, Intel White Paper, [3] Cheng Ding, Chen Yueguo, Cheng Weiwei, A Survey Study on Trust Management in P2P Systems, Technical Report, [4] Xue Chen, Guisheng Chen, Jin Liu, Xiangfeng Luo, Xuhui Li, and Bing Li, Trust Factors in P2P Networks, published in proceedings of the IEEE International Workshop on Semantic Computing and Systems (WSCS '08), pp 49-54, [5] Alvaro Arenas, Michael Wilson, Brian Matthews, On Trust Management in Grids, Invited paper in ICST 2007 [6] Yao Wang, Julita Vassileva, Bayesian Network-Based Trust Model, Published in Proceedings of IEEE/WIC International Conference on Web Intelligence WI '03, pp , [7] Dubey J., and Tokekar V., A Framework for Pure Peer-to-Peer Computing System, Published in Proceedings of Ninth IEEE and IFIP International Conference Wireless and Optical Communications Networks WOCN2012 Next Generation Internet, pp. 1-5, [8] Prashant Dewan, Partha Dasgupta, Securing P2P Networks Using Peer Reputations: Is there a Silver bullet?, Published in Proceedings of Second IEEE International Conference on, Consumer Communications and Networking Conference CCNC 2005, pp , [9] Stefan Kraxberger, Udo Payer, Security concept for Peer-to-Peer Systems, Published in Proceeding of the International Conference on Wireless Communications and Mobile Computing: Connecting the World Wirelessly IWCMC '09, pp , [10] Ajay Ravichandran, and Jongpil Yoon, Trust Management with Delegation in Grouped Peer-to-Peer Communities, Proceeding of the eleventh ACM symposium on Access control models and technologies SACMAT '06, pp , [11] Jung-Tae Kim, Hae-Kyeong,Park, Eui-Hyun Paik, Security Issues in Peer-to-Peer Systems, Published in proceedings of 7th International Conference on Advanced Communication Technology, ICACT 2005, Volume: 2, Page(s): , [12] Tu Chao, Hu Chaoju, and Li Shengli, An Improved Trust Model Based on Reputation in P2P Networks, Published in Proceedings of IEEE Asia-Pacific Conference on Wearable Computing Systems 2010, pp , [13] Jigyasu Dubey, Dr. (Mrs.) Vrinda Tokekar, Identification of Reliable Peer Groups in Peer-to-Peer Computing Systems, Published in proceedings of Third International Conference on Advances in Communication, Network, and Computing CNC 2012 and LNICST Vol. 108, pp , [14] Jigyasu Dubey, Dr. (Mrs.) Vrinda Tokekar, Identification of Efficient Peers in P2P Computing Systems for Real Time Applications, Published in International Journal of Peer to Peer Networks (IJP2P) Vol.3, No 6, pp , November (b) Fig. 2 (a) Comparison of Trust Values of TP3 with BN & BN with TW (b) Comparison of Trust Values of TP5 with BN & BN with TW.