Biblio

Network dynamics, such as bandwidth fluctuation and unexpected latency, hurt users' quality of experience (QoE) greatly for media services over the Internet. In this work, we propose a neural adaptive transport (NAT) framework to tackle the network dynamics for Internet-scale interactive media services. The entire NAT system has three major components: a learning based cloud overlay routing (COR) scheme for the best delivery path to bypass the network bottlenecks while offering the minimal end-to-end latency simultaneously; a residual neural network based collaborative video processing (CVP) system to trade the computational capability at client-end for QoE improvement via learned resolution scaling; and a deep reinforcement learning (DRL) based adaptive real-time streaming (ARS) strategy to select the appropriate video bitrate for maximal QoE. We have demonstrated that COR could improve the user satisfaction from 5% to 43%, CVP could reduce the bandwidth consumption more than 30% at the same quality, and DRL-based ARS can maintain the smooth streaming with \textbackslashtextless; 50% QoE improvement, respectively.

Bitcoin is the leading example of a blockchain application that facilitates peer-to-peer transactions without the need for a trusted intermediary. This paper considers possible attacks related to the decentralized network architecture of Bitcoin. We perform a data driven study of Bitcoin and present possible attacks based on spatial and temporal characteristics of its network. Towards that, we revisit the prior work, dedicated to the study of centralization of Bitcoin nodes over the Internet, through a fine-grained analysis of network distribution, and highlight the increasing centralization of the Bitcoin network over time. As a result, we show that Bitcoin is vulnerable to spatial, temporal, spatio-temporal, and logical partitioning attacks with an increased attack feasibility due to network dynamics. We verify our observations by simulating attack scenarios and the implications of each attack on the Bitcoin . We conclude with suggested countermeasures.

In order to ensure the security of electric power supervisory control and data acquisition (SCADA) system, this paper proposes a dynamic awareness security protection model based on security policy, the design idea of which regards safety construction protection as a dynamic analysis process and the security policy should adapt to the network dynamics. According to the current situation of the power SCADA system, the related security technology and the investigation results of system security threat, the paper analyzes the security requirements and puts forward the construction ideas of security protection based on policy protection detection response (P2DR) policy model. The dynamic awareness security protection model proposed in this paper is an effective and useful tool for protecting the security of power-SCADA system.

Consider n anonymous nodes each initially supporting an opinion in \1, 2, …, k\ and suppose that they should all learn the opinion with the largest support. Per round, each node contacts a random other node and exchanges B bits with it, where typically B is at most O(log n). This basic distributed computing problem is called the plurality consensus problem (in the gossip model) and it has received extensive attention. An efficient plurality protocol is one that converges to the plurality consensus as fast as possible, and the standard assumption is that each node has memory at most polylogarithmic in n. The best known time bound is due to Becchetti et al. [SODA'15], reaching plurality consensus in O(k log n) rounds using log(k+1) bits of local memory, under some mild assumptions. As stated by Becchetti et al., achieving a poly-logarithmic time complexity remained an open question. Resolving this question, we present an algorithm that with high probability reaches plurality consensus in O(log k log n) rounds, while having message and memory size of log k + O (1) bits. This even holds under considerably more relaxed assumptions regarding the initial bias (towards plurality) compared to those of prior work. The algorithm is based on a very simple and arguably natural mechanism.