No abstract

Most of the existing service discovery methods focus on finding candidate services based on functional and non-functional requirements. However, while the open science community engenders many similar scientific services, how to differentiate them remains a challenge. This paper proposes a trust model that leverages the implicit human factor to help quantify the trustworthiness of candidate services. A hierarchical Knowledge-Social-Trust (KST) network model is established to draw hidden information from various publication repositories (e.g., DBLP) and social networks (e.g., Twitter). As a proof of concept, a prototyping service has been developed to help scientists evaluate and visualize trust of services. The performance factor is studied and experience is reported.

In today's cellular networks, user equipment (UE) have suffered from low spectral efficiency at cell-edge region due to high interference from adjacent base stations (BSs), which share the same spectral radio resources. In the recently proposed cooperative cellular networks, geographically separated multiple BSs cooperate on transmission in order to improve the UE's signal-to-interference-plus-noise-ratio (SINR) at cell-edge region. The service provider of the system dynamically assigns the cluster of BSs to achieve higher SINR for the UE while optimizing the use of system radio resources. Although it is the service provider that makes the the clustering decision for the UE, the service provider relies on the UE's input to the decision; i.e., the channel states from the adjacent BSs to the UE. In essence, the operation of the cooperative cellular netwokrs heavily relies on the trust in the UEs. In this paper, we propose a new selfish attack against the cooperative cellular networks; an adversary reprograms her UE to report fabricated channel information to cause the service provider to make a decision that benefits the adversary while wasting its system resources. We evaluate the proposed attack in a cooperative cellular network having various performance goals on the simulation-based experiments and show that the adversary can trick the service provider into expending 3.7 times more radio resources for the adversary and, accordingly, the adversary achieves up to 16 dB SINR gain. Finally, we propose a thresholdbased countermeasure for the service provider to detect the attack with approximately 90% of accuracy.