Social trust and recommendation services are the most popular social rating systems today for service providers to learn about the social opinion or popularity of a product, item, or service, such as a book on Amazon, a seller on eBay, a story on Digg or a movie on Netflix. Such social rating systems are very convenient and offer alternative learning environments for decision makers, but they open the door for attackers to manipulate the social rating systems by selfishly promoting or maliciously demoting certain items. Although a fair amount of effort has been made to understand various risks and possible defense mechanisms to counter such attacks, most of the existing work to date has been devoted to studying specific types of attacks and their countermeasures. In this article, we argue that vulnerabilities in social rating systems and their countermeasures should be examined and analyzed in a systematic manner. We first give an overview of the common vulnerabilities and attacks observed in some popular social rating services. Next, we describe three types of attack strategies in two types of social rating systems, including a comprehensive theoretical analysis of their attack effectiveness and attack costs. Three context-aware countermeasures are then presented: (i) hiding user-item relationships, (ii) using confidence weight to distinguish popular and unpopular items, and (iii) incorporating time windows in trust establishment. We also provide an in-depth discussion on how these countermeasures can be used effectively to improve the robustness and trustworthiness of the social rating services.

This paper presents MAPS - a personalized Multi-Attribute Probabilistic Selection framework - to estimate the probability of an item being a user's best choice and rank the items accordingly. The MAPS framework makes three original contributions in this paper. First, we capture the inter-attribute t

With the popularity of voting systems in cyberspace, there is growing evidence that current voting systems can be manipulated by fake votes. This problem has attracted many researchers working on guarding voting systems in two areas: relieving the effect of dishonest votes by evaluating the trust of voters, and limiting the resources that can be used by attackers, such as the number of voters and the number of votes. In this paper, we argue that powering voting systems with trust and limiting attack resources are not enough. We present a novel attack named as Reputation Trap (RepTrap). Our case study and experiments show that this new attack needs much less resources to manipulate the voting systems and has a much higher success rate compared with existing attacks. We further identify the reasons behind this attack and propose two defense schemes accordingly. In the first scheme, we hide correlation knowledge from attackers to reduce their chance to affect the honest voters. In the second scheme, we introduce robustness-of-evidence, a new metric, in trust calculation to reduce their effect on honest voters. We conduct extensive experiments to validate our approach. The results show that our defense schemes not only can reduce the success rate of attacks but also significantly increase the amount of resources an adversary needs to launch a successful attack.

User behavior models are important for building realistic simulation environment for research on P2P multimedia file-sharing systems. In this paper, we build a user download behavior model and a user removal behavior model, which can describe important user characteristics that are not captured in the existing models. The proposed download behavior model incorporates retry behavior, and the removal behavior model integrates free-riding, file usage, and file removal. Based on two-year real user logs, we derive the range of all the model parameters and generate many interesting observations. To validate the proposed models, we compare several models in a case study on the number of living replicas in P2P file-sharing systems. The results demonstrate the accuracy, usability, and advantage of the proposed models.

Reputation systems are playing critical roles in securing today's distributed computing and communication systems. Similar to other security mechanisms, reputation systems can be under attack. In this paper, we report the discovery of a new attack, named RepTrap(Reputation Trap), against feedback-based reputation systems, such as those used in P2P file-sharing systems and E-commerce websites(e.g. Amazon.com). We conduct an in-depth investigation on this new attack, including analysis, case study, and performance evaluation based on real data and realistic user behavior models. We discover that the RepTrap is a strong and destructive attack that can manipulate the reputation scores of users, objects, and even undermine the entire reputation system. Compared with other known attacks that achieve the similar goals, the RepTrap requires less effort from the attackers and causes multi-dimensional damage to the reputation systems.

In probabilistic packet marking(PPM) for IP tracebacking, the number of packets that are needed to reconstruct the attacking paths depends on the precision of the attacking paths. In this paper, a Fast Two Phrases(FTP) PPM for IP tracebacking is proposed, which depends on the division of Autonomous System(AS) and two algorithms are used to reconstruct the attacking paths. It can reconstruct the exactly attacking paths between AS when it has received tens of packets, and reconstruct the attacking paths in AS after receiving more packets. This method can reduce the number of packets that are needed to reconstruct the attacking paths to the lowest while reducing the complexity of packet marking and reconstructing.