Planning for Survivable Networks

2024-04-19 02:38

本文主要是介绍Planning for Survivable Networks,希望对大家解决编程问题提供一定的参考价值,需要的开发者们随着小编来一起学习吧!

版权声明:原创作品,允许转载,转载时请务必以超链接形式标明文章原始出版、作者信息和本声明。否则将追究法律责任。 http://blog.csdn.net/topmvp - topmvp
Keep your network safe from security disasters with a dependable recovery strategy Companies are finally learning that a network disaster recovery plan is mandatory in these times, and they must be prepared to make difficult choices about network security. In the information-packed pages of this book, Annlee Hines shares her unique and diverse work experience. She explains that the first thing you need, whatever your business may be, is reliable information and an idea of what you need to protect, as well as what you are protecting it from. She then dives into a discussion of how much you can expect to spend depending on what kind of security your network requires. She also delves into addressing the variables that determine why your needs will not necessarily be the needs of your closest competitor. Most importantly, Hines writes this valuable material realizing that you already know how to do your job --it's just that you now have to reconsider just how vulnerable the information nervous system of your company really is. From major terrorist attacks to natural disasters to hackers, Annlee Hines explores how to defend your network and reviews such topics as: * Probes, viruses, worms, and Trojan horses * The most common vulnerabilities networks face * Understanding and justifying costs * Lessons to be learned from successful defense strategies * Preparing for the worst and the requirements of network survival * Remedies, cyber recovery, and restoration
http://www.megaupload.com/?d=W0WHOT64
http://www.oxyshare.com/get/991958600448fbd9a310ab1.96711153/Planning_for_Survivable_Networks.rar.html

这篇关于Planning for Survivable Networks的文章就介绍到这儿,希望我们推荐的文章对编程师们有所帮助!



http://www.chinasem.cn/article/916442

相关文章

A Comprehensive Survey on Graph Neural Networks笔记

一、摘要-Abstract 1、传统的深度学习模型主要处理欧几里得数据(如图像、文本),而图神经网络的出现和发展是为了有效处理和学习非欧几里得域(即图结构数据)的信息。 2、将GNN划分为四类:recurrent GNNs(RecGNN), convolutional GNNs,(GCN), graph autoencoders(GAE), and spatial–temporal GNNs(S

Complex Networks Package for MatLab

http://www.levmuchnik.net/Content/Networks/ComplexNetworksPackage.html 翻译: 复杂网络的MATLAB工具包提供了一个高效、可扩展的框架,用于在MATLAB上的网络研究。 可以帮助描述经验网络的成千上万的节点,生成人工网络,运行鲁棒性实验,测试网络在不同的攻击下的可靠性,模拟任意复杂的传染病的传

Convolutional Neural Networks for Sentence Classification论文解读

基本信息 作者Yoon Kimdoi发表时间2014期刊EMNLP网址https://doi.org/10.48550/arXiv.1408.5882 研究背景 1. What’s known 既往研究已证实 CV领域著名的CNN。 2. What’s new 创新点 将CNN应用于NLP,打破了传统NLP任务主要依赖循环神经网络(RNN)及其变体的局面。 用预训练的词向量(如word2v

【机器学习】生成对抗网络(Generative Adversarial Networks, GANs)详解

🌈个人主页: 鑫宝Code 🔥热门专栏: 闲话杂谈| 炫酷HTML | JavaScript基础 ​💫个人格言: "如无必要,勿增实体" 文章目录 生成对抗网络(Generative Adversarial Networks, GANs)详解GANs的基本原理GANs的训练过程GANs的发展历程GANs在实际任务中的应用小结 生成对

Image Transformation can make Neural Networks more robust against Adversarial Examples

Image Transformation can make Neural Networks more robust against Adversarial Examples 创新点 1.旋转解决误分类 总结 可以说简单粗暴有效

吴恩达深度学习笔记:卷积神经网络(Foundations of Convolutional Neural Networks)1.9-1.10

目录 第四门课 卷积神经网络(Convolutional Neural Networks)第一周 卷积神经网络(Foundations of Convolutional Neural Networks)1.9 池化层(Pooling layers)1.10 卷 积 神 经 网 络 示 例 ( Convolutional neural network example) 第四门课

AI - Planning

Motion PlanningCollision-free Path Collision Free Check Matlab Figure for Box Bounding Test MethodLine Segments Test Method Configuration Space C-Space Motion Planning 使用States建立State Gr

NLP-文本匹配-2016:SiamseNet【Learning text similarity with siamese recurrent networks】

NLP-文本匹配-2016:SiamseNet【Learning text similarity with siamese recurrent networks】

GNN-频域-2014:Spectral Networks and Locally Connected Networks on Graphs(频谱图卷积神经网络)【第一篇从频域角度分析】

《原始论文:Spectral Networks and Locally Connected Networks on Graphs》 空域卷积非常直观地借鉴了图像里的卷积操作,但缺乏一定的理论基础。 而频域卷积则不同,相比于空域卷积而言,它主要利用的是**图傅里叶变换(Graph Fourier Transform)**实现卷积。 简单来讲,它利用图的**拉普拉斯矩阵(Laplacian ma

YOLO前篇---Real-Time Grasp Detection Using Convolutional Neural Networks

论文地址:https://arxiv.org/abs/1412.3128 1. 摘要 比目前最好的方法提高了14%的精度,在GPU上能达到13FPS 2. 基于神经网络的抓取检测 A 结构 使用AlexNet网络架构,5个卷积层+3个全连接层,卷积层有正则化和最大池化层网络结构示意图如下 B 直接回归抓取 最后一个全连接层输出6个神经元,前4个与位置和高度相关,另外2个用来表示方向