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SecuredTrust: A Dynamic Trust Computation Model for Secured Communication in Multiagent Systems

Platform : java

IEEE Projects Years : 2012 - 13

SecuredTrust: A Dynamic Trust Computation Model for Secured Communication

in Multiagent Systems

Abstract

 

Security and privacy issues have become critically important with the fast expansion of multiagent systems. Most network applications such as pervasive computing, grid computing, and P2P networks can be viewed as multiagent systems which are open, anonymous, and dynamic in nature. Such characteristics of multiagent systems introduce vulnerabilities and threats to providing secured communication. One feasible way to minimize the threats is to evaluate the trust and reputation of the interacting agents. Many trust/reputation models have done so, but they fail to properly evaluate trust when malicious agents start to behave in an unpredictable way. Moreover, these models are ineffective in providing quick response to a malicious agent’s oscillating behavior. Another aspect of multiagent systems which is becoming critical for sustaining good service quality is the even distribution of workload among service providing agents. Most trust/reputation models have not yet addressed this issue. So, to cope with the strategically altering behavior of malicious agents and to distribute workload as evenly as possible among service providers; we first analyze the different factors related to evaluating the trust of an agent and then propose a comprehensive quantitative model for measuring such trust. We also propose a novel load-balancing algorithm based on the different factors defined in our model. Simulation results indicate that our model compared to other existing models can effectively cope with strategic behavioral change of malicious agents and at the same time efficiently distribute workload among the service providing agents under stable condition.

 

EXISTING SYSTEM:

 

Most of the existing global reputation models can successfully isolate malicious agents when the agents behave in a predictable way. However, these models suffer greatly when agents start to show dynamic personality, i.e., when they start to behave in a way that benefits them. These models also fail to adapt to the abrupt change in agents’ behavior and as a result suffer when agents alter their activities strategically.  Moreover, some of the models show little effect in dealing with more complex attacks such as dishonest or unfair rating and collusion. Another aspect which is slowly becoming critical for the proper maintenance of service quality is the appropriate distribution of workload among the trusted service providers. Without a proper load-balancing scheme, the load at highly reputable service providers will be immense which will eventually cause a bottleneck in the system’s service quality. To the best of our knowledge, none of the existing trust models consider load balancing among service providers.

 

PROPOSED SYSTEM:

We propose a feedback-based dynamic trust computation model named SecuredTrust which can effectively detect sudden strategic alteration in malicious behavior with the additional feature of balancing workload among service providers. Secured-Trust considers variety of factors in determining the trust of an agent such as satisfaction, similarity, feedback credibility, recent trust, historical trust, sudden deviation of trust, and decay of trust. We have used a novel policy of utilizing exponential averaging function to reduce storage overhead in computing the trust of agents. We have also proposed a new load-balancing algorithm based on approximate calculation of workload present at different service providers.

SECURE-TRUST MODULES:

 

 LOGIN TRUST

 

 MULTIAGENT SYSTEM

 

 LOAD BALANCING

 

 TRANSACTIONS

 

LOGIN TRUST:

                    Secure Trust in multiagent system for used. They are number of agents are used to trust and secured communication in multiagent system.Login module used for existing Agent logged in and when new Agent coming enter to Agent registration after generated automatic Agent id and user password. Agent should be know the automatic agent id because agent id is an username for logging in enter to the network.

MULTIAGENT SYSTEM:

An agent depends on some pretrusted agents for trust evaluation in absence of trustworthy recommenders. Even though EigenTrust may work well in social network infrastructure where pretrusted neighbors (agents) are likely to be trustworthy, but in the case of other multiagent systems like P2P, EigenTrust poses a few problems. First, in P2P network, such predetermined trustworthy agents are not readily vailable. Second, depending on these pretrusted agents creates a vulnerability in the sense that if some of these pre-trusted agents get compromised, then it will be much easier to launch a large-scale malicious attack. The trust model proposed by Wen is similar to EigenTrust, but it does not consider the use of pretrusted agents in the calculation of trust. Dou’s model reduces iteration cost and punishes malicious behavior, but does not consider the punishment of dishonest recommenders.

LOAD BALANCING:

We propose an algorithm for balancing loads among the trusted agents. For selective scenario, we first compute the trust of agents who respond to a transaction request and then we select the agent with the highest trust value. However, in this scenario, the agent with the highest trust value will have immense workload while other capable agents with slightly lower reputation will have considerably less workload. The problem that will arise from this disproportionate allocation of workload is that the quality of service will fall greatly due to the heavy workload present at the highly trusted agents. So, a load-balancing algorithm

is required for sustaining good service quality.

TRANSACTIONS:

In the trust prioritized setting, an agent first initiates a transaction request. Against each request, certain percentage of agents responds. The response percentage is controlled by response_rate parameter. The initiating agent then sorts the responders based on their trust value and selects the agent with the highest trust value to perform the desired transaction. Finally, in the load-balancing scheme, a service provider with least amount of workload is selected.

            A multiagent is use an agent first initiates a transaction request.An agent is transaction to the one to another agent to transaction.The agents are of mainly two types—good and malicious. Good agents cooperate in providing both good service and honest feedback. In contrast, malicious agents are opportunistic in the sense that they cheat whenever it is advantageous for them. Malicious agents provide both ineffective service and false feedback.

Hardware Requirements:

  • System                  : Pentium IV 2.4 GHz.
  • Hard Disk             : 40 GB.
  • Monitor                 : 15 VGA Colour.
  • Mouse                   : Logitech.
  • Ram                       : 256 Mb.

SOFTWARE SPECIFICATION:

  • Operating System      :            Windows XP
  • Software                    :            JAVA (JDK 1.6), Net Beans IDE 6.8

 

 



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