Ad hoc networks rely on the cooperation of the nodes participating in the network to forward packets for each other. A node may decide not to cooperate to save its resources while still using the network to relay its traffic. If too many nodes exhibit this behavior, network performance degrades and cooperating nodes may find themselves unfairly loaded. Most previous efforts to counter this behavior have relied on further cooperation between nodes to exchange reputation information about other nodes. If a node observes another node not participating correctly, it reports this observation to other nodes who then take action to avoid being affected and potentially punish the bad node by refusing to forward its traffic. Unfortunately, such second-hand reputation information is subject to false accusations and requires maintaining trust relationships with other nodes. The objective of OCEAN is to avoid this trust-management machinery and see how far we can get simply by using direct first-hand observations of other nodes' behavior. We find that, in many scenarios, OCEAN can do as well as, or even better than, schemes requiring second-hand reputation exchanges. This encouraging result could possibly help obviate solutions requiring trust-management for some contexts.
CONFIDANT @cite_10 also detects misleading nodes by means of observation and more aggressively informs other nodes of this misbehavior through reports sent around the network. Each node in the network hosts a monitor for observations, reputation records for first-hand and trusted second-hand reports, trust records to control the trust assigned to received warnings, and a path manager used by nodes to adapt their behavior according to reputation information. In more recent work @cite_11 @cite_19 , these researchers find that reputation schemes can be beneficial for fast misbehavior detection, but only when one can deal with false accusations, for which they propose a solution using Bayesian statistics. Our goal is to avoid the machinery for managing these reports and their associated trust issues entirely.
@cite_19 @cite_10 @cite_11
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Some misbehavior detection and reputation systems in mobile ad-hoc networks rely on the dissemination of information of observed behavior, which makes them vulnerable to false accusations. This vulnerability could be removed by forbidding the dissemination of information on observed behavior in the first place, but, as we show here, this has more drawbacks than a solution that allows dissemination and copes with false accusations. We propose a method for reducing the impact of false accusations. In our approach, nodes collect first-hand information about the behavior of other nodes by direct observation. In addition, nodes maintain a rating about every other node that they care about, in the form of a continuous variable per node. From time to time nodes exchange their first-hand information with others, but, using the Bayesian approach we designed and present in this paper, only second-hand information that is not incompatible with the current rating is accepted. Ratings are slightly modified by accepted information. The reputation of a given node is the collection of ratings maintained by others about this node. By means of simulation we evaluated the robustness of our approach against several types of adversaries that spread false information, and its efficiency at detecting malicious nodes. The simulation results indicate that our system largely reduces the impact of false accusations, while still benefiting from the accelerated detection of malicious nodes provided by second-hand information. We also found that when information dissemination is not used, the time until malicious nodes are detected can be unacceptable. Mobile ad-hoc networking works properly only if the participating nodes cooperate in routing and forwarding. However, it may be advantageous for individual nodes not to cooperate. We propose a protocol, called CONFIDANT, for making misbehavior unattractive; it is based on selective altruism and utilitarianism. It aims at detecting and isolating misbehaving nodes, thus making it unattractive to deny cooperation. Trust relationships and routing decisions are based on experienced, observed, or reported routing and forwarding behavior of other nodes. The detailed implementation of CONFIDANT in this paper assumes that the network layer is based on the Dynamic Source Routing (DSR) protocol. We present a performance analysis of DSR fortified by CONFIDANT and compare it to regular defenseless DSR. It shows that a network with CONFIDANT and up to 60 of misbehaving nodes behaves almost as well as a benign network, in sharp contrast to a defenseless network. All simulations have been implemented and performed in GloMoSim. Mobile ad-hoc networks rely on the cooperation of nodes for routing and forwarding. For individual nodes there are however several advantages resulting from noncooperation, the most obvious being power saving. Nodes that act selfishly or even maliciously pose a threat to availability in mobile adhoc networks. Several approaches have been proposed to detect noncooperative nodes. In this paper, we investigate the effect of using rumors with respect to the detection time of misbehaved nodes as well as the robustness of the reputation system against wrong accusations. We propose a Bayesian approach for reputation representation, updates, and view integration. We also present a mechanism to detect and exclude potential lies. The simulation results indicate that by using this Bayesian approach, the reputation system is robust against slander while still benefitting from the speed-up in detection time provided by the use of rumors. Nodes in Mobile Ad hoc Networks (MANETs) are required to relay data packets to enable communication between other nodes that are not in radio range with each other. However, whether for selfish or malicious reasons, a node may fail to cooperate during the network operations or even attempt to disturb them, both of which have been recognized as misbehaviors. Various trust management schemes have been studied to assess the behaviors of nodes so as to detect and mitigate node misbehaviors inMANETs. Most of existing schemes model a node's trustworthiness along a single dimension, combining all of the available evidence to calculate a single, scalar trust metric. A single measure, however, may not be expressive enough to adequately describe a node's trustworthiness in many scenarios. In this paper, we describe a multi-dimensional framework to evaluate the trustworthiness of MANET node from multiple perspectives. Our scheme evaluates trustworthiness from three perspectives: collaboration trust, behavioral trust, and reference trust. Different types of observations are used to independently derive values for these three trust dimensions. We present simulation results that illustrate the effectiveness of the proposed scheme in several scenarios.