网御安全网关:批处理工具与ipython交互计算可视化Cookbook

需积分: 50 7 下载量 69 浏览量 更新于2024-08-09 收藏 1.49MB PDF 举报
"网御安全网关PowerVWeb界面在线手册" 本文档主要介绍了网御百兆安全网关的批处理工具及其使用方法,这是一款用于交互式计算和可视化的ipython工具的专业指南,旨在帮助用户更高效地管理和配置网络安全设备。在许可方面,用户可以上传并导入安全网关模块的许可证。 批处理工具提供了以下功能: 1. **导出配置命令**:用户可以选择导出包过滤规则和资源定义的命令,导出的命令可以直接在系统中导入。包过滤规则会保存到 `/tmp/pf.log`,资源定义则保存到 `/tmp/addserver.log`。 2. **查看历史记录**:用户可以查看之前执行过的命令行历史记录。 3. **清除历史记录**:点击“清空”按钮,可以清除所有历史记录。 4. **下载历史记录**:用户可以通过“导出”功能下载历史记录。 5. **编辑命令行批处理文件**:用户可以在编辑框中编写命令,每行一个命令,支持 `sleep` 和 `beep` 命令。点击“重写”可重新编辑,点击“提交”将命令提交至安全网关。 6. **上载批处理文件**:用户可以选择本地的批处理文件,通过“导入”功能将其上传至安全网关。 7. **执行批处理**:提交或上载后的批处理文件,通过点击“执行批处理”来执行。未执行的命令显示为蓝色,执行中可以点击“取消执行”中断批处理。 在执行批处理时,用户需要注意以下几点: - 不要在执行过程中关闭或刷新弹出窗口。 - 每条命令需顶格写,不应有前导空格。 - `sleep` 和 `beep` 命令的格式为 `keyword + 空格 + 秒数`,如 `sleep 2`。 - 必须先导出资源,再导出规则,因为规则可能依赖于资源,否则可能导致批处理失败。 此文档属于网御安全网关PowerVWeb界面在线手册的一部分,由北京网御星云信息技术有限公司编写。手册强调了版权信息,同时指出公司对产品和手册内容不做额外的保证,并对使用或无法使用产品导致的任何损害不负赔偿责任。 手册还涵盖了快速配置、系统配置等章节,包括管理主机、管理方式、内网地址、接入方式(如宽带固定IP和ADSL拨号)、网关设置、拨号设备、规则设置、系统信息(如日期时间、系统参数、双系统配置)、管理配置(管理主机、管理员账号、管理证书和管理方式)、系统更新(系统升级和导入导出)等内容。这些章节详细阐述了如何配置和管理安全网关,以确保网络环境的安全与稳定。
2018-02-24 上传
We are becoming awash in the flood of digital data from scientific research, engineering, economics, politics, journalism, business, and many other domains. As a result, analyzing, visualizing, and harnessing data is the occupation of an increasingly large and diverse set of people. Quantitative skills such as programming, numerical computing, mathematics, statistics, and data mining, which form the core of data science, are more and more appreciated in a seemingly endless plethora of fields. Python, a widely-known programming language, is also one of the leading open platforms for data science. IPython is a mature Python project that provides scientist-friendly interactive access to Python. It is part of the broader Project Jupyter, which aims to provide high-quality environments for interactive computing, data analysis, visualization, and the authoring of interactive scientific documents. Jupyter is estimated to have several million users today. The prequel of this book, Learning IPython for Interactive Computing and Data Visualization Second Edition, Packt Publishing was published in 2015, two years after the first edition. It is a beginner-level introduction to data science and numerical computing with Python, IPython, and Jupyter. This book, the first edition of which was published in 2014, continues that journey by presenting more than 100 recipes for interactive scientific computing and data science. These recipes not only cover programming topics such as numerical computing, high-performance computing, parallel computing, and interactive visualization, but also data analysis topics such as statistics, data mining, machine learning, signal processing, graph theory, numerical optimization, and many others. This second edition is fully compatible with the latest versions of the platform and its libraries. It includes new recipes to better leverage the latest features of Python 3, and it introduces promising new projects such as JupyterLab, Altair, and Dask.