计算机网络安全防范技术分析

经济管理研究, 2020

在市场经济推动、科学技术支持下，企业会计信息化、网络虚拟化进程越来越快，为企业管理、决策工作提供了依据，企业可以在共享平台中获取更多的信息资源，优化了信息孤岛的问题。在信息化时代，一体化信息模式为企业节省了很多的人力和物力资源，针对更多的信息资源进行了整合，论文主要针对大数据时代下企业会计信息化风险分析及防范措施进行分析，希望能为企业节省更多的人力、物力资源，提升企业的综合竞争实力。

智能城市应用, 2019

近几年来，化工行业不断发展，科学技术也随之更新，在生产过程中使用的电气仪表数量不断增加，基于此，针对石油化工电气仪表安全供电系统进行深层次的分析。本文基于石油化工中电气仪表供电系统的特点，结合电气仪表供电系统存在的安全问题，有针对性的提出了提高系统安全的主要对策，以供参考。

Computer Science and Application, 2018

In order to solve the problem of feature dimensionality in current malicious code detection, this paper proposes a method of feature selection of malicious code based on information gain algorithm which introduces frequency weight factor and genetic algorithm. This method can select the optimal feature subset that can effectively distinguish between normal code and malicious code, and achieve dimensionality reduction. This method uses the strong global search ability of genetic algorithm to search the feature subset. At the same time, the information gain algorithm based on the frequency weighting factor is used as the fitness evaluation of the feature subset. At last, we use a variety of popular classifiers to learn and verify. Experiments show that this method can effectively reduce the dimensions of features in malicious code detection and effectively improve the learning efficiency and accuracy of the classifier.

Journal of Risk Analysis and Crisis Response, 2017

The network platform connecting multiple agents, which is embedded in the models to integrate individual wisdoms into a great wisdom, is called Internet of intelligences (IOI). The risk radar can be driven by IOI with mathematical models written by PHP language. A measure space of the seismic macro-anomalies group was set up in IOI based the consensus of the staffs participating earthquake monitoring and prediction. The core technology in IOI is information diffusion technology, and its intelligent development depends on the appearance of intelligent mathematics on earth. By means of the development of the factor space theory to construct IOI expressing knowledge and being cognizant of thinking, we should promote the research of "Internet + Risk Analysis" and promote the development of Web mathematics, and lay the foundation for intelligent mathematics. The IOI supported by Web mathematics is expected to decompose and dissolve wisdom ingredients in some factor spaces so that individual wisdoms are integrated to be a great wisdom.

Chinese Science Bulletin, 2009

智能城市应用, 2019

随着网络信息化建设的迅速发展，传统的网络架构分为核心层、汇聚层和接入层。核心层是高速网络交换的枢纽，对整个数据网络的连接起到重要的作用。文章提出利用网络扁平化提高网络质量推进智慧新时代网络的发展。

Advances in Education, 2015

The major of Digital Media Technology of independent college in China mainly aims to train applied talents with reasonable knowledge structure, while the experience of teaching management and the construction of teaching system of independent colleges are not well organized due to their short history. Therefore, the question of what method this major can use to educate high quality applied talents with the ability of processing digital media information becomes the focus of research and discussion in independent colleges. This paper provides elaboration on the characteristics of practical teaching, the construction mode of practical teaching of the major of Digital Media Technology, and the exploration and measure of practical teaching of the major of Digital Media Technology in Binhai College, Nankai University.

SCIENTIA SINICA Technologica, 2011

Zhongguo kexue, 2016

中国科学 : 信息科学 第 46 卷 第 2 期 基于密度的聚类算法 DBSCAN (density based spatial clustering of applications with noise) [11] 能发现 任意形状的簇, 在邻域半径参数 ϵ 和核心对象邻域包含的最少样本数参数 MinPts 设置适当时, 能快 速发现含噪声空间中任意形状的类簇 [1, 6, 8] , 但如何设置这两个参数缺乏理论依据. 近邻传播聚类算 法 AP (affinity propagation) [4] 将所有样本看作网络中的一个顶点, 通过反复迭代交换近邻样本间的 信息, 寻找最优的类代表点样本集合, 使所有样本与最近类代表点样本的相似度之和最大, 发现数据 集样本的类簇分布. AP 算法具有简单、高效的优点, 特别是在类别数目较多情况下, 该算法具有非常 好的聚类效果 [12] , 但是该算法不能发现任意形状的簇. 基于同步动力学模型 Kuramoto 的层次聚类算 法 [8] 无需人工设置任何参数, 可以检测出任意数量、形状和大小的类簇, 但层次聚类算法的错误累积 缺陷无法避免. 2014 年 6 月 Science 发表了自动确定类簇数和类簇中心的新聚类算法 DPC (clustering by fast search and find of density peaks) [5] , 该算法能快速发现任意形状数据集的密度峰值点 (即类簇中心),

Kexue tongbao, 2008