ID 原文 译文
44916 针对超密集蜂窝网络中大规模机器类通信所涉及的通信与计算问题，提出了一种基于 Markov 预测的切换方案。 In order to solve the problem of the communication and computational problems of large-scale machine communication in an ultra-dense cellular network, a Markov prediction based handover scheme (MPHS) was proposed.
44917 首先考虑半结构化的中心控制的异构网络设计，该设计中含有密集部署的虚拟节点，以实现低成本和高效率的覆盖。 Firstly, a kind of heterogeneous network design with semi-structure and central control was considered which contains the densely deployed virtual nodes and thus realized a low cost and efficient coverage.
44918 该网络可以根据用户的移动性及网络通信量动态调整接入点。 The network can dynamically adjust the access point according to the user's mobility and network traffic.
44919 其次构建 Markov 模型，引入负载感知思想， Secondly, a Markov model was constructed, and the idea of load-aware was introduced.
44920 通过权衡信号质量与小区负载，有效地预测用户的下一个最优接入点。 By weighing the signal quality and the cell load, the user's next optimal access point was effectively predicted.
44921 仿真实验结果证明了该方案用于小区切换预测的可行性与有效性。 The simulation results show the feasibility and effectiveness of the proposed scheme for cell handover predicting.
44922 针对目前网络大数据环境攻击检测中因某些攻击步骤样本的缺失而导致攻击模型训练不够准确的问题，以及现有集成分类器在构建多级分类器时存在的不足，提出基于多层集成分类器的恶意网络流量检测方法。 A malicious network traffic detection method based on multi-level distributed ensemble classifier was proposed for the problem that the attack model was not trained accurately due to the lack of some samples of attack steps for detecting attack in the current network big data environment, as well as the deficiency of the existing ensemble classifier in the construction of multilevel classifier.
44923 该方法首先采用无监督学习框架对数据进行预处理并将其聚成不同的簇，并对每一个簇进行噪音处理，然后构建一个多层集成分类器 MLDE 检测网络恶意流量。 The dataset was first pre-processed and aggregated into different clusters, then noise processing on each cluster was performed, and then a multi-level distributed ensemble classifier,MLDE, was built to detect network malicious traffic.
44924 MLDE 集成框架在底层使用基分类器，非底层使用不同的集成元分类器。 In the MLDE ensemble framework the base classifier was used at the bottom, while the non-bottom different ensemble classifiers were used.
44925 该框架构建简单， The framework was simple to be built.