No abstract

Virtual communities become more and more heterogeneous as their scale increases. This implies that, rather than being a single, homogeneous community, they become a collection of knots (or sub-communities) of users. For the computation of a member's reputation to be useful, the system must therefore identify the community knot to which this member belongs and to interpret its reputation data correctly. Unfortunately, to the best of our knowledge existing trust-based reputation models treat a community as a single entity and do not explicitly address this issue. In this paper, we introduce the knot-aware trust-based reputation model for large-scale virtual communities. We define a knot as a group of community members having overall "strong" trust relations between them. Different knots typically represent different view points and preferences. It is therefore plausible that the reputation of the same member in different knots assign may differ significantly. Using our knot-aware approach, we can deal with heterogeneous communities where a member's reputation may be distributed in a multi modal manner. As we show, an interesting and beneficial feature of our knot-aware model is that it naturally prevents malicious attempts to bias community members' reputation.

Abstract. Trust and Reputation systems in distributed environments attain widespread interest as online communities are becoming an inherent part of the daily routine of Internet users. Trust-based models enable safer operation within communities to which information exchange and peer to peer interaction are centric. Several models for trust based reputation have been suggested recently, among them the Knots model [5]. In these models, the subjective reputation of a member is computed using information provided by a set of members trusted by the latter. The present paper discusses the computation of reputation in such models, while preserving members' private information. Three different schemes for the private computation of reputation are presented, and the advantages and disadvantages in terms of privacy and communication overhead are analyzed.

Abstract. The Domain Name System (DNS) is an essential component of the internet infrastructure that translates domain names into IP addresses. Recent incidents verify the enormous damage of malicious activities utilizing DNS such as bots that use DNS to locate their command & control servers. Detecting malicious domains using the DNS network is therefore a key challenge. We project the famous expression Tell me who your friends are and I will tell you who you are, motivating many social trust models, on the internet domains world. A domain that is related to malicious domains is more likely to be malicious as well. In this paper, our goal is to assign reputation values to domains and IPs indicating the extent to which we consider them malicious. We start with a list of domains known to be malicious or benign and assign them reputation scores accordingly. We then construct a DNS based graph in which nodes represent domains and IPs. Our new approach for computing domain reputation applies a flow algorithm on the DNS graph to obtain the reputation of domains and identify potentially malicious ones. The experimental evaluation of the flow algorithm demonstrates its success in predicting malicious domains.

Trust and reputation systems for virtual communities are gaining increasing research attention. These systems track members' activities and obtain their reputation to improve the quality of member interactions and reduce the effect of fraudulent members. As virtual communities become a central playground for internet users, the reputation a member gains within a community may be viewed as a social credential. These credentials can serve the user as a means for promoting her status in new communities on one hand, and on the other hand assist virtual communities to broaden their knowledge about users with relatively low activity volume. The Cross-Community Reputation (CCR) model was designed for sharing reputation knowledge across communities. The model identifies the fundamental terms that are required for a meaningful sharing of reputation information between communities and proposes methods to make that information sharing feasible within the boundaries of users' and communities' policies. This paper presents the CR model and draws the architecture guidelines for designing an infrastructure to support it. The proposed model is evaluated by using a sample of real-world users' ratings as well as by conducting a dedicated experiment with real users. The results of the experimental evaluation demonstrate the effectiveness of the CCR model in various aspects.