BGP Neighbor Trust Establishment Mechanism Based on the Bargaining Game

The Border Gateway Protocol (BGP) is the standard inter-domain route protocol on the Internet. Autonomous System (AS) traffic is forwarded by the BGP neighbors. In the route selection, if there are malicious or inactive neighbors, it will affect the network’s performance or even cause the network to crash. Therefore, choosing trusted and safe neighbors is an essential part of BGP security research. In response to such a problem, in this paper we propose a BGP Neighbor Trust Establishment Mechanism based on the Bargaining Game (BNTE-BG). By combining service quality attributes such as bandwidth, packet loss rate, jitter, delay, and price with bargaining game theory, it allows the AS to select trusted neighbors which satisfy the Quality of Service independently. When the trusted neighbors are forwarding data, we draw on the gray correlation algorithm to calculate neighbors’ behavioral trust and detect malicious or inactive BGP neighbors.

Positive Development in Latin American Emerging Adults

This chapter presents some empirical evidence on the strikingly high level of life satisfaction, happiness, and optimism that young people in Latin America enjoy, comparable to those of the United States or the Netherlands, despite the difficult social, political, and economic context in which they are embedded. Moreover, around half of them flourish regardless their scarce social participation beyond the network of family members, friends, and trusted neighbors. Higher Latin American social closeness and support (rewarding current and past close relationships with family and friends) seem to be the main reason for these youths’ well-being and flourishing.

E-Commerce Recommending Model Based on Trust Community

To responses to the current information "overload" problem widespread in e-commerce systems, a new approach, using the method of user clustering, node trust value analyzing and product evaluating is put forward to build an e-commerce trust community for e-commerce recommendation. According to some of the most trusted neighbors` evaluation information for goods, the recommendation model predicts the score of goods that the users have purchased, to recommend items which have a higher value score, to a customer. In the proposed recommending algorithm, the time effect of recommendation is taken into consideration to provide effective recommending services for users.