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数据驱动的网络分析

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数据驱动的网络分析

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传统的入侵检测和日志文件分析已经不再足以保护当今的复杂网络。在本书中,安全研究人员Michael Collins展示了多种收集和分析网络流量数据集的技术和工具。你将从中理解网络的使用方式,以及保护和改进它所必需的行动。
《数据驱动的网络分析》分为3个部分,讲解了收集和组织数据的过程、各种分析工具和多种不同的分析场景和技术。对于熟悉脚本的网络管理员和运营安全分析人员来说,本书是他们的理想读物。
《数据驱动的网络分析》内容如下:
探索捕捉安全数据的网络、主机和服务传感器;
使用关系数据库、图解数据库、Redis和Hadoop存储数据流量;
使用SiLK、R语言和其他工具进行分析和可视化;
通过探索性数据分析检测不寻常的现象;
用图解分析识别网络中的重要结构;
确定网络中穿越服务端口的流量;
检查通信量和行为,以发现DDoS和数据库攫取;
获得网络映射和库存盘点的详细过程。
Content Description

传统的入侵检测和日志文件分析已经不再足以保护当今的复杂网络,本书讲解了多种网络流量数据集的采集和分析技术及工具,借助这些工具,可以迅速定位网络中的问题,并采取相应的行动,保障网络的运行安全。《数据驱动的网络分析》分为3部分,共15章,内容包括数据采集的常规过程,用于采集网络流量的传感器,基于特定系统的传感器,数据存储和分析,使用互联网层次知识系统(SiLK)分析NetFlow数据,用于安全分析的R语言简介、入侵检测系统的工作机制以及实施,确定实施攻击的幕后真凶,探索性数据分析以及数据可视化,检查通信流量和行为,获取网络映射和库存盘点的详细过程等。《数据驱动的网络分析》适合网络安全工程师和网络管理人员阅读。
Author Description

Michael Collins,是RedJack有限责任公司的首席科学家,该公司是华盛顿首都特区的一家网络安全和数据分析公司。在任职于RedJacak之前,Collins博士是卡内基·梅隆大学CERT/网络态势感知小组的成员。他的主要研究方向是网络测量和流量分析,特别是大流量数据集的分析。Collins博士于2008年毕业于卡内基·梅隆大学,获得电子工程博士学位。他的硕士和学士学位也来自于同一学校。
Comments

本书全面介绍了充分利用各种可用工具,将收集的数据集合成为可操作信息的过程。各个水平的网络分析人员都会从Michael讲解的分析技术中受益匪浅。
——Andre DiMino,网络安全工程师,乔治 华盛顿大学

令人吃惊的是,一个成熟的行业仍然苦于缺乏介绍网络分析和取证的书籍。Collins先生的著作是安全分析这一重要领域中的指路明灯。
——Rabbi Rob Thomas,CEO,Team Cymru
Catalogue

第1部分 数 据
第1章 传感器和探测器简介 3
1.1 观察点:传感器的位置对数据采集的影响 4
1.2 领域:确定可以采集的数据 7
1.3 操作:传感器对数据所做的处理 10
1.4 小结 12
第2章 网络传感器 13
2.1 网络分层及其对测量的影响 14
2.1.1 网络层次和观察点 16
2.1.2 网络层次和编址 19
2.2 封包数据 20
2.2.1 封包和帧格式 21
2.2.2 滚动缓存 21
2.2.3 限制每个封包中捕捉的数据 21
2.2.4 过滤特定类型封包 21
2.2.5 如果不是以太网怎么办 25
2.3 NetFlow 26
2.3.1 NetFlow v5格式和字段 26
2.3.2 NetFlow生成和采集 28
第3章 主机和服务传感器:在源上的流量日志 29
3.1 访问和操纵日志文件 30
3.2 日志文件的内容 32
3.2.1 优秀日志消息的特性 32
3.2.2 现有日志文件以及处理方法 34
3.3 有代表性的日志文件格式 36
3.3.1 HTTP:CLF和ELF 36
3.3.2 SMTP 39
3.3.3 Microsoft Exchange:邮件跟踪日志 41
3.4 日志文件传输:转移、Syslog和消息队列 43
3.4.1 转移和日志文件留存 43
3.4.2 syslog 43
第4章 用于分析的数据存储:关系数据库、大数据和其他选项 46
4.1 日志数据和CRUD范式 47
4.2 NoSQL系统简介 49
4.3 使用何种存储方法 52

第2部分 工 具
第5章 SiLK套件 56
5.1 SiLK的概念和工作原理 56
5.2 获取和安装SiLK 57
5.3 选择和格式化输出字段操作:rwcut 58
5.4 基本字段操纵:rwfilter 63
5.4.1 端口和协议 63
5.4.2 大小 65
5.4.3 IP地址 65
5.4.4 时间 66
5.4.5 TCP选项 67
5.4.6 助手选项 68
5.4.7 杂项过滤选项和一些技巧 69
5.5 rwfileinfo及出处 69
5.6 合并信息流:rwcount 72
5.7 rwset和IP集 74
5.8 rwuniq 77
5.9 rwbag 79
5.10 SiLK高级机制 79
5.11 采集SiLK数据 81
5.11.1 YAF 81
5.11.2 rwptoflow 83
5.11.3 rwtuc 84
第6章 R安全分析简介 86
6.1安装与设置 86
6.2R语言基础知识 87
6.2.1R提示符 87
6.2.2R变量 88
6.2.3编写函数 93
6.2.4条件与循环 95
6.3使用R工作区 97
6.4数据帧 98
6.5可视化 101
6.5.1可视化命令 101
6.5.2可视化参数 101
6.5.3可视化注解 103
6.5.4导出可视化 104
6.6分析:统计假设检验 104
6.6.1假设检验 105
6.6.2检验数据 107
第7章 分类和事件工具:IDS、AV和SEM 110
7.1IDS的工作原理 110
7.1.1基本词汇 111
7.1.2分类器失效率:理解“基率谬误” 114
7.1.3应用分类 116
7.2提高IDS性能 117
7.2.1改进IDS检测 118
7.2.2改进IDS响应 122
7.2.3预取数据 122
第8章 参考和查找:了解“某人是谁”的工具 124
8.1 MAC和硬件地址 124
8.2 IP编址 126
8.2.1 IPv4地址、结构和重要地址 126
8.2.2 IPv6地址、结构和重要地址 128
8.2.3 检查连接性:使用ping连接到某个地址 129
8.2.4 路由跟踪 131
8.2.5 IP信息:地理位置和人口统计学特征 132
8.3 DNS 133
8.3.1 DNS名称结构 133
8.3.2 用dig转发DNS查询 134
8.3.3 DNS反向查找 142
8.3.4 使用whois查找所有者 143
8.4 其他参考工具 146
第9章 其他工具 148
9.1可视化 148
9.2通信和探查 151
9.2.1netcat 151
9.2.2nmap 153
9.2.3Scapy 154
9.3封包检查和参考 157
9.3.1Wireshark 157
9.3.2GeoIP 157
9.3.3NVD、恶意软件网站和C*E 158
9.3.4搜索引擎、邮件列表和人 160

第3部分 分 析
第10章 探索性数据分析和可视化 162
10.1 EDA的目标:应用分析 163
10.2 EDA工作流程 165
10.3 变量和可视化 166
10.4 单变量可视化:直方图、QQ图、箱线图和等级图 167
10.3.1 直方图 167
10.3.2 柱状图(不是饼图) 169
10.3.3 分位数-分位数(Quantile-Quantile ,QQ)图 170
10.3.4 五数概括法和箱线图 172
10.3.5 生成箱线图 173
10.5 双变量描述 175
10.5.1 散点图 175
10.5.2 列联表 177
10.6 多变量可视化 177
第11章 摸索 185
11.1 攻击模式 185
11.2 摸索:错误的配置、自动化和扫描 187
11.2.1 查找失败 187
11.2.2 自动化 188
11.2.3 扫描 188
11.3 识别摸索行为 189
11.3.1 TCP摸索:状态机 189
11.3.2 ICMP消息和摸索 192
11.3.3 识别UDP摸索 193
11.4 服务级摸索 193
11.4.1 HTTP摸索 193
11.4.2 SMTP摸索 195
11.5 摸索分析 195
11.5.1 构建摸索警报 196
11.5.2 摸索行为的取证分析 196
11.5.3 设计一个网络来利用摸索 197
第12章 通信量和时间分析 199
12.1 工作日对网络通信量的影响 199
12.2 信标 201
12.3 文件传输/攫取 204
12.4 局部性 206
12.4.1 DDoS、突发拥塞和资源耗尽 209
12.4.2 DDoS和路由基础架构 210
12.5 应用通信量和局部性分析 214
12.5.1 数据选择 214
12.5.2 将通信量作为警报 216
12.5.3 将信标作为警报 216
12.5.4 将局部性作为警报 217
12.5.5 工程解决方案 217
第13章 图解分析 219
13.1 图的属性:什么是图 219
13.2 标签、权重和路径 222
13.3 分量和连通性 227
13.4 聚类系数 228
13.5 图的分析 229
13.5.1 将分量分析作为警报 229
13.5.2 将集中度分析用于取证 230
13.5.3 广度优先搜索的取证使用 231
13.5.4 将集中度分析用于工程 232
第14章 应用程序识别 234
14.1 应用程序识别机制 234
14.1.1 端口号 234
14.1.2 通过标志抓取识别应用程序 238
14.1.3 通过行为识别应用程序 241
14.1.4 通过附属网站识别应用程序 244
14.2 应用程序标志:识别和分类 245
14.2.1 非Web标志 245
14.2.2 Web客户端标志:User-Agent字符串 246
第15章 网络映射 249
15.1 创建一个初始网络库存清单和映射 249
15.1.1 创建库存清单:数据、覆盖范围和文件 250
15.1.2 第1阶段:前3个问题 251
15.1.3 第2阶段:检查IP空间 254
15.1.4 第3阶段:识别盲目和难以理解的流量 258
15.1.5 第4阶段:识别客户端和服务器 261
15.2 更新库存清单:走向连续审计 263

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