Trust is an integral component in many kinds of human interaction, allowing people to act under uncertainty and with the risk of negative consequences. For example, exchanging money for a service, giving access to your property, and choosing between conflicting sources of information all may utilize some form of trust. In computer science, trust is a widelyused term whose definition differs among researchers and application areas. Trust is an essential component of the vision for the Semantic Web, where both new problems and new applications of trust are being studied. This paper gives an overview of existing trust research in computer science and the Semantic Web.

The majority of existing trust models is based on three underlying assumptions: (i) proven identity of agents, (ii) repetitive interactions and (iii) similar trusting situations. In our work, we address these assumptions by introduction of simple classification techniques in our mechanism that extends existing trust models, rather than by introduction of a new model. The proposed approach formalizes the situation (context) and/or trusted agent identity in a multi-dimensional Identity-Context space, and attaches the trustworthiness evaluations to individual elements from this metric space, rather than to fixed identity tags (e.g. AIDs, addresses). Trustworthiness of the individual elements of the Identity-Context space can be evaluated using any trust model that supports weighted aggregations and updates, allowing the integration of the mechanism with most existing work. Trust models with the proposed extension are appropriate for deployment in dynamic, ad-hoc and mobile environments, where the agent platform can not guarantee the identity of the agents and where the cryptography-based identity management techniques may be impractical due to the unreliable and costly communication.

Efficiently handling reputation is important in dealing with free-riding, malicious attacks and random failures in selforganized communication systems. At the same time, work in this context is often found to be relevant in many other disciplines, in particular the social sciences. A number of distributed reputation systems have been proposed and analyzed, although research has not been very coherent. In this paper, for the first time, we provide an overview of the stateof-the-art in the various computer science communities as well as the social sciences. In particular, we present results obtained from our mathematical model devised to investigate the impact of liars on their peers ’ reputation about a subject. We find that liars have no impact unless their number exceeds a certain threshold (phase transition). We give precise formulae and quantify the impact, thereby providing insight into fundamental questions in social networks as well as facilitating performance evaluation and optimization of distributed reputation systems in communication networks. We conclude by suggesting fundamental directions for future research into reputation. Categories and Subject Descriptors C.2.1 [Computer-communication networks]: Network architecture and design—Distributed networks; D.2.4 [Computer-communication networks]: Distributed systems—

In this paper, we present a model of a trust-based recommendation system on a social network. The idea of the model is that agents use their social network to reach information and their trust relationships to filter it. We investigate how the dynamics of trust among agents affect the performance of the system by comparing it to a frequency-based recommendation system. Furthermore, we identify the impact of network density, preference heterogeneity among agents, and knowledge sparseness to be crucial factors for the performance of the system. The system self-organises in a state with performance near to the optimum; the performance on the global level is an emergent property of the system, achieved without explicit coordination from the local interactions of agents. 1

Trust is a crucial basis for interactions among parties in large, open systems. Yet, the scale and dynamism of such systems make it infeasible for each party to have a direct basis for trusting another party. For this reason, the participants in an open system must share information about trust. However, they should not automatically trust such shared information. This paper studies the problem of propagating trust in multiagent systems. It describes a new algebraic approach, shows some theoretical properties of it, and empirically evaluates it on two social network datasets. This evaluation incorporates a new methodology that involves dealing with opinions in an evidential setting. 1

Abstract. We present a trust-based mechanism for the acquisition of information from possibly unreliable sources. Our mechanism addresses the case where the acquired information cannot be verified. The idea is to intersperse questions (“challenges”) for which the correct answers are known. By evaluating the answers to these challenges, probabilistic conclusions about the correctness of the unverifiable information can be drawn. Less challenges need to be used if an information provider has shown to be trustworthy. This work focuses on three major issues of such a mechanism. First, how to estimate the correctness of the unverifiable information. Second, how to determine an optimal number of challenges. And finally, how to establish trust and use it to reduce the number of challenges. Our approach can resist collusion and shows great promise for various application areas such as distributed computing or peer-to-peer networks.

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Abstract. This paper presents a design of high-performance agentbased intrusion detection system designed for deployment on high-speed network links. To match the speed requirements, wire-speed data acquisition layer is based on hardware-accelerated NetFlow like probe, which provides overview of current network traffic. The data is then processed by detection agents that use heterogenous anomaly detection methods. These methods are correlated by means of trust and reputation models, and the conclusions regarding the maliciousness of individual network flows is presented to the operator via one or more analysis agents, that automatically gather supplementary information about the potentially malicious traffic from remote data sources such as DNS, whois or router configurations. Presented system is designed to help the network operators efficiently identify malicious flows by automating most of the surveillance process. 1

In multi-agent systems, agents must typically interact with others to achieve their goals. Since agents are assumed to be self-interested, it is important to choose reliable interaction partners to maximise the likelihood of success. Sub-standard and failed interactions can result from a poor selection. Effective partner selection requires information about how agents behave in varying situations, and such information can be obtained from others in the form of recommendations as well as through direct experience. In open and dynamic environments, agents face quick and unforeseen changes to the behaviour of others and the population itself. This paper presents a trust and reputation model which allows agents to adapt quickly to changes in their environment. Our approach combines components from several existing models to determine trust using direct experiences and recommendations from others. We build upon previous models by considering the multi-dimensionality of trust, recency of information, and dynamic selection of recommendation providers. Specifically, we take a multi-dimensional approach to evaluating both direct interactions and recommendations. Recommendation sharing includes information about the recency and nature of interactions, which allows an evaluator to assess relevance, and to select recommenders themselves based on trust.

Abstract. We present a method that improves the results of network intrusion detection by integration of several anomaly detection algorithms through trust and reputation models. Our algorithm is based on existing network behavior analysis approaches that are embodied into several detection agents. We divide the processing into three distinct phases: anomaly detection, trust model update and collective trusting decision. Each of these phases contributes to the reduction of classification error rate, by aggregation of anomaly values provided by individual algorithms, individual update of each agent’s trust model based on distinct traffic representation features (derived from its anomaly detection model), and re-aggregation of the trustfulness data provided by individual agents. The result is a trustfulness score for each network flow, which can be used to guide the manual inspection, thus significantly reducing the amount of traffic to analyze. To evaluate the effectiveness of the method, we present a set of experiments performed on real network data. 1