Disruptive Security Technologies with Mobile Code

本书介绍了一种采用主动方式应对攻击的颠覆性安全技术,通过适应性系统减轻病毒和蠕虫带来的威胁。书中详细探讨了移动代码、健壮的点对点网络等主题,并介绍了网络流量的多分形模型、安全自动机、可靠性、服务质量等内容。
 Book Description The traditional fortress mentality of system security has proven ineffective to attacks by disruptive technologies. This is due largely to their reactive nature. Disruptive security technologies, on the other hand, are proactive in their approach to attacks. They allow systems to adapt to incoming threats, removing many of the vulnerabilities exploited by viruses and worms. Disruptive Security Technologies With Mobile Code and Peer-To-Peer Networks provides a foundation for developing these adaptive systems by describing the design principles and the fundamentals of a new security paradigm embracing disruptive technologies. In order to provide a thorough grounding, the author covers such topics as mobile code, robust peer-to-peer networks, the multi-fractal model of network flow, security automata, dependability, quality of service, mobile code paradigms, code obfuscation, and distributed adaptation techniques as part of system security. Adaptive systems allow network designers to gain equal footing with attackers. This complete guide combines a large body of literature into a single volume that is concise and up to date. With this book, computer scientists, programmers, and electrical engineers, as well as students studying network design will dramatically enhance their systems' ability to overcome potential security threats. # 400 pages # Publisher: CRC; 1 edition (November 29, 2004) # Language: English # ISBN-10: 0849322723 # ISBN-13: 978-0849322723 http://rapidshare.com/files/86953811/0849322723.pdf
内容概要:本文介绍了ENVI Deep Learning V1.0的操作教程,重点讲解了如何利用ENVI软件进行深度学习模型的训练与应用,以实现遥感图像中特定目标(如集装箱)的自动提取。教程涵盖了从数据准备、标签图像创建、模型初始化与训练,到执行分类及结果优化的完整流程,并介绍了精度评价与通过ENVI Modeler实现一键化建模的方法。系统基于TensorFlow框架,采用ENVINet5(U-Net变体)架构,支持通过点、线、面ROI或分类图生成标签数据,适用于多/高光谱影像的单一类别特征提取。; 适合人群:具备遥感图像处理基础,熟悉ENVI软件操作,从事地理信息、测绘、环境监测等相关领域的技术人员或研究人员,尤其是希望将深度学习技术应用于遥感目标识别的初学者与实践者。; 使用场景及目标:①在遥感影像中自动识别和提取特定地物目标(如车辆、建筑、道路、集装箱等);②掌握ENVI环境下深度学习模型的训练流程与关键参数设置(如Patch Size、Epochs、Class Weight等);③通过模型调优与结果反馈提升分类精度,实现高效自动化信息提取。; 阅读建议:建议结合实际遥感项目边学边练,重点关注标签数据制作、模型参数配置与结果后处理环节,充分利用ENVI Modeler进行自动化建模与参数优化,同时注意软硬件环境(特别是NVIDIA GPU)的配置要求以保障训练效率。
评论
成就一亿技术人!
拼手气红包6.0元
还能输入1000个字符
 
红包 添加红包
表情包 插入表情
 条评论被折叠 查看
添加红包

请填写红包祝福语或标题

红包个数最小为10个

红包金额最低5元

当前余额3.43前往充值 >
需支付:10.00
成就一亿技术人!
领取后你会自动成为博主和红包主的粉丝 规则
hope_wisdom
发出的红包
实付
使用余额支付
点击重新获取
扫码支付
钱包余额 0

抵扣说明:

1.余额是钱包充值的虚拟货币,按照1:1的比例进行支付金额的抵扣。
2.余额无法直接购买下载,可以购买VIP、付费专栏及课程。

余额充值