Momentum-kick model application to high multiplicity pp collisions at s=13 TeV\sqrt{s}=13\,\mathrm{TeV} at the LHC

In this study, the momentum-kick model is used to understand the ridge behaviours in dihadron $\Delta\eta$--$\Delta\varphi$ correlations recently reported by the LHC in high-multiplicity proton-proton (pp) collisions. The kick stand model is based on a momentum kick by leading jets to partons in the medium close to the leading jets. The medium where partons move freely is assumed in the model regardless of collision systems. This helps us apply the method to small systems like pp collisions in a simple way. Also, the momentum transfer is purely kinematic and this provides us a strong way to approach the ridge behaviour analytically. There are already several results with this approach in high-energy heavy-ion collisions from the STAR and PHENIX at RHIC and from the CMS at LHC. The momentum-kick model is extended to the recent ridge results in high-multiplicity pp collisions with the ATLAS and CMS at LHC. The medium property in high-multiplicity pp collisions is diagnosed with the result of the model.Comment: 10 pages, 2 tables and 3 figure

Integrated Social and Quality of Service Trust Management of Mobile Groups in Ad Hoc Networks

Abstract—We propose to combine social trust derived from social networks with quality-of-service (QoS) trust derived from communication networks to obtain a composite trust metric as a basis for evaluating trust of mobile nodes in mobile ad hoc network (MANET) environments. We develop a novel modelbased approach to identify the best protocol setting under which trust bias is minimized, that is, the peer-to-peer subjective trust as a result of executing our distributed trust management protocol is close to ground truth status over a wide range of operational and environment conditions with high resiliency to malicious attacks and misbehaving nodes. Keywords—trust management; mobile ad hoc networks; QoS trust; social trust; trust bias minimization. I

Optimal deployments of defense mechanisms for the internet of things

Internet of Things (IoT) devices can be exploited by the attackers as entry points to break into the IoT networks without early detection. Little work has taken hybrid approaches that combine different defense mechanisms in an optimal way to increase the security of the IoT against sophisticated attacks. In this work, we propose a novel approach to generate the strategic deployment of adaptive deception technology and the patch management solution for the IoT under a budget constraint. We use a graphical security model along with three evaluation metrics to measure the effectiveness and efficiency of the proposed defense mechanisms. We apply the multi-objective genetic algorithm (GA) to compute the {\em Pareto optimal} deployments of defense mechanisms to maximize the security and minimize the deployment cost. We present a case study to show the feasibility of the proposed approach and to provide the defenders with various ways to choose optimal deployments of defense mechanisms for the IoT. We compare the GA with the exhaustive search algorithm (ESA) in terms of the runtime complexity and performance accuracy in optimality. Our results show that the GA is much more efficient in computing a good spread of the deployments than the ESA, in proportion to the increase of the IoT devices