Behavior-aware Trust Reasoning Based on Associate Petri Net

INTRODUCTION

Trust issue is an important problem both in social field and in information field and it is a cognitive phenomenon of human society, which can reflect the basic belief of an individual to human nature. In social network, trust relationship is the core of interpersonal relationship and the trust relationships dependent on each other constitute the so-called web of trust (Mogens et al., 2007).

The researches related to trust are the main emphasis in social science field all the time, but they paid more attention to the qualitative research in the field of humanities. On the whole, for a long time trust is looked as a cognitive phenomenon, which is subjective, imprecise and unreliable, even is distrusted. Therefore, scientific and systematic researches about trust are lacked, most of all, it lacks the formal and quantitative research. With the open network represented by Internet arising and developing, it becomes more and more urgent to study trust by formal method (Yin et al., 2010).

Trust is divided into identity trust and behavior trust and only identity trust is considered in the typical security, which mainly involves the identity authentication of user and server and resource access control after authorized. Behavior trust is defined as the evaluation of behavior coming from the interaction among two or more entities. Modeling the behavior trust of an entity is aim to study how to formally define, evaluate and deduce the trust level in open network environment (El-Salamouny et al., 2009).

Diego (1990) defined the trust by mathematics method for the first time and trust is defined as the subjective possibility prediction of object’s specific behavior from subject, which depends on the experience and will be revised continuously along with the variance of object’s behavior result. The definition reflects the uncertainty of trust and lays the theoretical foundation for the representation of trust by mathematics method.

Theodorakopoulos and Baras (2006) interpret the concept of trust as a relation among entities that participate in various protocols. Trust relations are based on evidence created by the previous interactions of entities within a protocol. The evaluation process is modeled as a path problem on a directed graph, where nodes represent entities and edges represent trust relations. Using the theory of semirings, the paper shows how two nodes can establish an indirect trust relation without previous direct interaction. Adnane et al. (2008) proposed for OLSR the integration of trust reasoning into each node behavior, so as to allow a self-organized trust-based control to help nodes to detect misbehavior attacks. The analysis of OLSR brings out the trust rules that characterize this protocol and allows us to express formally the trust-related properties that can be verified by each node to assess the correct behavior of the other nodes. Simulation of OLSR with nodes reasoning on trust allows us to demonstrate the effectiveness of our approach and to compare trust-based routing choices with the bare OLSR reachability-based choices. Ray et al. (2009) proposed a model that allows us to formalize trust relationships, the trust relationship between a truster and a trustee is associated with a context and depends on the experience, knowledge and recommendation that the truster has with respect to the trustee in the given context. Sometimes enough information is not available about a given context to evaluate trust, formalizing the relationships between contexts allows us to extrapolate values from related contexts to approximate the trust of an entity even when all the information needed to calculate the trust is not available (Ray et al., 2009).

The methods mentioned above didn’t consider the behavior interaction between participants, while in the process of interaction between different local trust models, some special interaction behavior relationships may arise, such as behavior consistent relativity, behavior exclusion relativity, behavior interaction relativity, behavior controlled relativity. And for the different interaction property, there is different effect on the computation of trust. In this study, on the basis of analyzing the computing method of trust degree, associated Petri net is used to model the local trust model. Furthermore, combined with the behavior interaction relationships, computation policies about trust degree are presented aimed to different behavior relationships. At last, Discrete Particle Swarm Optimization (DPSO) is used to find the optimal trust value.

BEHAVIOR MODELING METHODS TRUST REASONING BASED ON APN

The concept of trust originally derives from sociology, so it possesses the subjectivity, generality and so on and the trust concept in sociology is not applicable for the user’s behavior trust in science. Therefore, we decompose user’s whole behavior trust step by step according to practical application requirements and functional characteristics, in detail, the comprehensive and general behavior trust can be decomposed into several behavior trust attributes, in Fig. 1. By means of this method the role can easily evaluate the trust attributes and the problem of generality and uncertainty of user’s behavior trust can be resolved effectively. For example, user’s behavior trust can be decomposed into security trust attribute, performance trust attribute and reliability trust attribute and so on.

There are some related researches about trust modeling and trust reasoning, for example, Bayes network model (Mogens et al., 2007) and based on it the probability of every node can be gotten through forward or backward reasoning. The modeling method not only owns theory foundation but also combines knowledge representation and knowledge reasoning together. On the one hand, the causal knowledge about the prediction of user’s behavior trust can be expressed naturally and intuitively by directed graph. On the other hand, static data about user’ behavior can be blended into the Bayes model in the form of condition probability.

Fig. 1:	Behavior trust attributes

Fig. 2:	Task activity graph (roles collaborating in activity)

Thus, Bayes network can integrate the prior knowledge and posterior data seamlessly, which are all used to represent user’s behavior, but it has also the limitation which does not consider the behavior interaction relationship between different roles, thereby, it makes the reasoning about trust more distrusted.

The proposed approach models a specify task by the APN (Shih et al., 2007). A task is composed of some activity with certain constraint relationships. Every activity is accomplished by several roles in Fig. 2, so we use APN to model every activity (called as local trust model), then these local trust model compose the global trust model by certain constraint relationships. In local trust model, the place nodes of the APN model represent the candidate roles, the support nodes represent constraint behavior attribute relationships between interaction roles and the transition nodes represent attribute trust value between interaction roles. The modeling methods about the trust evaluation of task based on the APN are presented as follows:

Fig. 3:	APN model of a role

EFFICIENT OPTIMIZATION ALGORITHM ABOUT SOLVING TRUST VALUE OF COMPLEX SYSTEM

For a complex system, lots of activities are needed and every activity needs some roles collaborating to accomplish the trust evaluation. When solving the trust value of total task, the time consuming is larger and error rate of the optimal trust value is higher. The paper makes full use of the properties of APN model, adopts DPSO methods (Fan et al., 2010) combined with APN. Concretely, regarding firing sequences of APN as chromosomes, every legal firing sequence in Petri Net model represent a feasible trust evaluation. It can make trust evaluation algorithm closely related to Petri Net models, which turns the problem of trust evaluation into the problem of searching for the best firing sequences based on the APN model. So the problem to be solved can be converted into the corresponding APN model, then the best firing sequence can be found by optimizing legal sequence with DPSO and in the searching process the problem can be tackled with the full use of the legal sequence algorithm of Petri net. If simply adopting Petri net (Murata, 1989) to do trust evaluation, it is very complex when the scale of model is comparatively large. If DPSO method is adopted only, searching has comparatively large randomness. It goes against resolving problems effectively. This method takes these two aspects into consideration simultaneously, which reduces the complication and searching randomness effectively.

The optimization methods solving trust evaluation of complex activity based DPSO and APN are as follows:

Step 1:

Initialize the particle population: According to the requirement of total task, we select fixed number (such as K) firing sequences from APN model. If it exists a firing sequences , which makes , then (p is corresponding to the matching component services)can be regarded as a particle, here, M0 is the first marking, Mf is the end marking:

to compute the v.

EXPERIMENT SIMULATION

In order to evaluate the proposed trust reasoning optimization algorithm, lots of experiments have been performed based on a series trust value of roles which are produced randomly.

The experiment is set as follows:

Test 1 For a given task which satisfies above experiment set, using fuzzy trust computing methods (fuzzy trust computing methods) presented in the literature (Zhang et al., 2008) and the behavior-aware trust reasoning methods based on APN (behavior-aware trust reasoning methods) respectively, we compare their time efficiency under different activity number.

In Fig. 4, the experiment effect shows the execution time consuming of behavior-aware trust reasoning methods is less than the fuzzy trust computing methods, when the more of the activity number, the more obvious of the effect. The reason is that we uses fully APN’s properties when DPSO locating in the APN model, which makes locating space lower and some roles which don’t satisfy constraint relationships or have minor associate relation need not to match each other. So it can save execution time and improve the time performance of trust reasoning evaluation.

Fig. 4:	The time cost of two methods

Fig. 5:	The error rate of two methods

Test 2 For a given task which satisfies above experiment set, using fuzzy trust computing methods (fuzzy trust computing methods) presented in the literature (Zhang et al., 2008) and the behavior-aware trust reasoning methods based on APN (behavior-aware trust reasoning methods) respectively, we compare their error rate:

under different activity number.

In Fig. 5, the experiment effect shows the error rate of behavior-aware trust reasoning methods is lower than the fuzzy trust computing methods, in the behavior-aware trust reasoning methods, the behavior interaction relationships between the local trust model are taken into account, the trust value of some non-consistency relationships is adjusted, which can avoid the blindness according to the model simply to computing.