S OF REPUTATION AND TRUST-BASED MODELS in Visual Studio .NET

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EXAMPLES OF REPUTATION AND TRUST-BASED MODELS
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observation. CORE does not differentiate between interaction and observation for subjective reputation unlike CONFIDANT [23]. The subjective reputation is calculated only for the neighboring nodes that is, nodes in the transmission range of the subject. The subjective reputation is updated only during the request phase. If a provider does not cooperate with a requestor s request, then a negative value is assigned to the rating factor of that observation, and consequently the reputation of the provider will decrease. The reputation value varies between 1 and 1. New nodes, when they enter the network, are also assigned a neutral reputation value of 0 since enough observations are not available to make an assessment of their reputation.
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Information Sharing. CORE uses indirect reputation that is, second-hand information to model MANETs as complex societies. Thus, a node s impression of another node is in uenced by the impression of other members of the society. However, there is a restriction imposed by CORE on the type of information propagated by the nodes. Only positive information can be exchanged. As we have stated before, this prevents bad-mouthing attacks on benign nodes. It is assumed that each reply message consists of a list of nodes that cooperated. Hence indirect reputation will be updated only during the reply phase.
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Information Modeling. CORE uses functional reputation to test the trustworthiness of a node with respect to different functions. Functional reputation is the combined value of subjective and indirect reputation for different functions. Different applications may assign different weight to routing and similarly to various other functions such as packet forwarding, and so on. The authors argue that reputation is compositional; that is, the overall opinion on a node that belongs to the network is obtained as a result of the combination of different types of evaluation. Thus, the global reputation for each node is obtained by combining the three types of reputation. Finally, reputation values that are positive are decremented along time to ensure that nodes cooperate and contribute on a continuing basis. This prevents a node from initially building up a good reputation by being very cooperative and contributive but then abusing the system later.
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Decision Making. When a node has to make a decision on whether or not to execute a function for a requestor, it checks the reputation value of that node. If the reputation value is positive, then it is a well-behaved entity. On the other hand, if the reputation value of the requestor is negative, then it is tagged as a misbehaving entity and denied the service. A misbehaving entity is denied service unless it cooperates and ameliorates its reputation to a positive value. Reputation is hard to build, because reputation gets decreased every time the watchdog detects a noncooperative behavior, and it also gets decremented over time to prevent malicious nodes from building reputation and then attacking the system resources.
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REPUTATION-AND-TRUST-BASED SYSTEMS FOR AD HOC NETWORKS
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Discussions. Giving greater weight to the past does enable a malicious node to misbehave temporarily if it has accumulated a high reputation value. Since only positive information is shared for indirect reputation updates, CORE prevents false accusation attacks, con ning the vulnerability of the system to only false praise. The authors argue that a misbehaving node has no advantage by giving false praise to other unknown entities. This is true only so long as malicious nodes are not colluding. When malicious nodes start collaborating, then they can help prolong the survival time of one another through false praise. However, the effect of false praise is mitigated in CORE to some extent by coupling the information dissemination to reply messages. Also, since only positive information is shared, the possibility of retaliation is prevented. There is an inherent problem in combining the reputation values for various functions into a single global value. This potentially helps a malicious node to hide its misbehavior with respect to certain functions by behaving cooperatively with respect to the remaining functions. The incentive for a node to misbehave with respect to a particular function is to save scarce resource. The node may choose to not cooperate for functions that consume lots of memory and/or power and choose to cooperate for functions that don t require as much memory and/or power. Nonetheless, functional reputation is a very nice feature of CORE that can be used to exclude nodes from functions for which their reputation value is below the threshold and include them for functions for which their reputation value is above the threshold. CORE also ensures that disadvantaged nodes that are inherently sel sh due to their critical energy conditions are not excluded from the network using the same criteria as for malicious nodes. Hence, an accurate evaluation of the reputation value is performed that takes into account sporadic misbehavior. Therefore, CORE minimizes false detection of a nodes misbehavior.
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13.6.2 CONFIDANT CONFIDANT stands for cooperation of nodes fairness in dynamic ad hoc networks. This model was proposed by Buchegger and Le Boudec [23] to make misbehavior unattractive in MANETs based on selective altruism and utilitarianism. CONFIDANT is a distributed, symmetric reputation model that uses both rst-hand and second-hand information for updating reputation values. It aims at detecting and isolating misbehaving nodes, thus making it unattractive to deny cooperation. In CONFIDANT, the Dynamic Source Routing (DSR) protocol has been used for routing and it assumes a promiscuous mode operation. CONFIDANT also assumes that no tamper-proof hardware is required for itself, since a malicious node neither knows its reputation entries in other nodes nor has access to other nodes to modify their values. CONFIDANT is inspired by The Sel sh Gene by Dawkins [23], which states reciprocal altruism is bene cial for every ecological system when favors are returned simultaneously because of instant grati cation. The bene t of behaving well is not so obvious in the case where there is a delay between granting a favor and the repayment. The CONFIDANT protocol consists of four components at each node: monitor, trust manager, reputation system, and path manager.
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