Preface |
|
ix | |
|
|
|
1 Sensors and Detectors: An Introduction |
|
|
3 | (12) |
|
Vantages: How Sensor Placement Affects Data Collection |
|
|
4 | (3) |
|
Domains: Determining Data That Can Be Collected |
|
|
7 | (3) |
|
Actions: What a Sensor Does with Data |
|
|
10 | (3) |
|
|
13 | (2) |
|
|
15 | (20) |
|
Network Layering and Its Impact on Instrumentation |
|
|
16 | (8) |
|
Network Layers and Vantage |
|
|
18 | (5) |
|
Network Layers and Addressing |
|
|
23 | (1) |
|
|
24 | (6) |
|
|
24 | (1) |
|
|
25 | (1) |
|
Limiting the Data Captured from Each Packet |
|
|
25 | (1) |
|
Filtering Specific Types of Packets |
|
|
25 | (4) |
|
What If It's Not Ethernet? |
|
|
29 | (1) |
|
|
30 | (3) |
|
NetFlow v5 Formats and Fields |
|
|
30 | (2) |
|
NetFlow Generation and Collection |
|
|
32 | (1) |
|
|
33 | (2) |
|
3 Host and Service Sensors: Logging Traffic at the Source |
|
|
35 | (20) |
|
Accessing and Manipulating Logfiles |
|
|
36 | (2) |
|
|
38 | (5) |
|
The Characteristics of a Good Log Message |
|
|
38 | (3) |
|
Existing Logfiles and How to Manipulate Them |
|
|
41 | (2) |
|
Representative Logfile Formats |
|
|
43 | (7) |
|
|
43 | (4) |
|
|
47 | (2) |
|
Microsoft Exchange: Message Tracking Logs |
|
|
49 | (1) |
|
Logfile Transport: Transfers, Syslog, and Message Queues |
|
|
50 | (3) |
|
Transfer and Logfile Rotation |
|
|
51 | (1) |
|
|
51 | (2) |
|
|
53 | (2) |
|
4 Data Storage for Analysis: Relational Databases, Big Data, and Other Options |
|
|
55 | (14) |
|
Log Data and the CRUD Paradigm |
|
|
56 | (3) |
|
Creating a Well-Organized Flat File System: Lessons from SiLK |
|
|
57 | (2) |
|
A Brief Introduction to NoSQL Systems |
|
|
59 | (3) |
|
What Storage Approach to Use |
|
|
62 | (7) |
|
Storage Hierarchy, Query Times, and Aging |
|
|
64 | (5) |
|
|
|
|
69 | (32) |
|
What Is SiLK and How Does It Work? |
|
|
69 | (1) |
|
Acquiring and Installing SiLK |
|
|
70 | (1) |
|
|
70 | (1) |
|
Choosing and Formatting Output Field Manipulation: rwcut |
|
|
71 | (5) |
|
Basic Field Manipulation: rwfilter |
|
|
76 | (7) |
|
|
77 | (1) |
|
|
78 | (1) |
|
|
78 | (2) |
|
|
80 | (1) |
|
|
80 | (2) |
|
|
82 | (1) |
|
Miscellaneous Filtering Options and Some Hacks |
|
|
82 | (1) |
|
Rwfileinfo and Provenance |
|
|
83 | (3) |
|
Combining Information Flows: rwcount |
|
|
86 | (2) |
|
|
88 | (3) |
|
|
91 | (2) |
|
|
93 | (1) |
|
|
93 | (2) |
|
|
93 | (2) |
|
|
95 | (5) |
|
|
96 | (2) |
|
|
98 | (1) |
|
|
98 | (2) |
|
|
100 | (1) |
|
6 An Introduction to R for Security Analysts |
|
|
101 | (28) |
|
|
102 | (1) |
|
|
102 | (11) |
|
|
102 | (2) |
|
|
104 | (5) |
|
|
109 | (2) |
|
Conditionals and Iteration |
|
|
111 | (2) |
|
|
113 | (1) |
|
|
114 | (3) |
|
|
117 | (4) |
|
|
117 | (1) |
|
Parameters to Visualization |
|
|
118 | (2) |
|
Annotating a Visualization |
|
|
120 | (1) |
|
|
121 | (1) |
|
Analysis: Statistical Hypothesis Testing |
|
|
121 | (6) |
|
|
122 | (2) |
|
|
124 | (3) |
|
|
127 | (2) |
|
7 Classification and Event Tools: IDS, AV, and SEM |
|
|
129 | (18) |
|
|
130 | (8) |
|
|
130 | (4) |
|
Classifier Failure Rates: Understanding the Base-Rate Fallacy |
|
|
134 | (2) |
|
|
136 | (2) |
|
Improving IDS Performance |
|
|
138 | (7) |
|
|
138 | (5) |
|
|
143 | (1) |
|
|
144 | (1) |
|
|
145 | (2) |
|
8 Reference and Lookup: Tools for Figuring Out Who Someone Is |
|
|
147 | (28) |
|
MAC and Hardware Addresses |
|
|
147 | (3) |
|
|
150 | (8) |
|
IPv4 Addresses, Their Structure, and Significant Addresses |
|
|
150 | (2) |
|
IPv6 Addresses, Their Structure and Significant Addresses |
|
|
152 | (1) |
|
Checking Connectivity: Using ping to Connect to an Address |
|
|
153 | (2) |
|
|
155 | (2) |
|
IP Intelligence: Geolocation and Demographics |
|
|
157 | (1) |
|
|
158 | (13) |
|
|
158 | (1) |
|
Forward DNS Querying Using dig |
|
|
159 | (8) |
|
|
167 | (1) |
|
Using who is to Find Ownership |
|
|
168 | (3) |
|
Additional Reference Tools |
|
|
171 | (4) |
|
|
171 | (4) |
|
|
175 | (16) |
|
|
175 | (3) |
|
|
175 | (3) |
|
Communications and Probing |
|
|
178 | (6) |
|
|
179 | (1) |
|
|
180 | (1) |
|
|
181 | (3) |
|
Packet Inspection and Reference |
|
|
184 | (4) |
|
|
184 | (1) |
|
|
185 | (1) |
|
The NVD, Malware Sites, and the C*Es |
|
|
186 | (1) |
|
Search Engines, Mailing Lists, and People |
|
|
187 | (1) |
|
|
188 | (3) |
|
|
|
10 Exploratory Data Analysis and Visualization |
|
|
191 | (30) |
|
The Goal of EDA: Applying Analysis |
|
|
193 | (1) |
|
|
194 | (2) |
|
Variables and Visualization |
|
|
196 | (1) |
|
Univariate Visualization: Histograms, QQ Plots, Boxplots, and Rank Plots |
|
|
197 | (10) |
|
|
198 | (2) |
|
Bar Plots (Not Pie Charts) |
|
|
200 | (1) |
|
The Quantile-Quantile (QQ) Plot |
|
|
201 | (2) |
|
The Five-Number Summary and the Boxplot |
|
|
203 | (1) |
|
|
204 | (3) |
|
|
207 | (4) |
|
|
207 | (3) |
|
|
210 | (1) |
|
Multivariate Visualization |
|
|
211 | (9) |
|
Operationalizing Security Visualization |
|
|
213 | (7) |
|
|
220 | (1) |
|
|
221 | (16) |
|
|
221 | (3) |
|
Fumbling: Misconfiguration, Automation, and Scanning |
|
|
224 | (2) |
|
|
224 | (1) |
|
|
225 | (1) |
|
|
225 | (1) |
|
|
226 | (5) |
|
TCP Fumbling: The State Machine |
|
|
226 | (3) |
|
ICMP Messages and Fumbling |
|
|
229 | (2) |
|
|
231 | (1) |
|
Fumbling at the Service Level |
|
|
231 | (2) |
|
|
231 | (2) |
|
|
233 | (1) |
|
|
233 | (3) |
|
|
234 | (1) |
|
Forensic Analysis of Fumbling |
|
|
235 | (1) |
|
Engineering a Network to Take Advantage of Fumbling |
|
|
236 | (1) |
|
|
236 | (1) |
|
12 Volume and Time Analysis |
|
|
237 | (24) |
|
The Workday and Its Impact on Network Traffic Volume |
|
|
237 | (3) |
|
|
240 | (3) |
|
|
243 | (3) |
|
|
246 | (10) |
|
DDoS, Flash Crowds, and Resource Exhaustion |
|
|
249 | (1) |
|
DDoS and Routing Infrastructure |
|
|
250 | (6) |
|
Applying Volume and Locality Analysis |
|
|
256 | (4) |
|
|
256 | (2) |
|
|
258 | (1) |
|
Using Beaconing as an Alarm |
|
|
259 | (1) |
|
Using Locality as an Alarm |
|
|
259 | (1) |
|
|
260 | (1) |
|
|
260 | (1) |
|
|
261 | (18) |
|
Graph Attributes: What Is a Graph? |
|
|
261 | (4) |
|
Labeling, Weight, and Paths |
|
|
265 | (5) |
|
Components and Connectivity |
|
|
270 | (1) |
|
|
271 | (2) |
|
|
273 | (4) |
|
Using Component Analysis as an Alarm |
|
|
273 | (2) |
|
Using Centrality Analysis for Forensics |
|
|
275 | (1) |
|
Using Breadth-First Searches Forensically |
|
|
275 | (2) |
|
Using Centrality Analysis for Engineering |
|
|
277 | (1) |
|
|
277 | (2) |
|
14 Application Identification |
|
|
279 | (16) |
|
Mechanisms for Application Identification |
|
|
279 | (12) |
|
|
280 | (3) |
|
Application Identification by Banner Grabbing |
|
|
283 | (3) |
|
Application Identification by Behavior |
|
|
286 | (4) |
|
Application Identification by Subsidiary Site |
|
|
290 | (1) |
|
Application Banners: Identifying and Classifying |
|
|
291 | (3) |
|
|
291 | (1) |
|
Web Client Banners: The User-Agent String |
|
|
292 | (2) |
|
|
294 | (1) |
|
|
295 | (18) |
|
Creating an Initial Network Inventory and Map |
|
|
295 | (16) |
|
Creating an Inventory: Data, Coverage, and Files |
|
|
296 | (1) |
|
Phase I The First Three Questions |
|
|
297 | (3) |
|
Phase II Examining the IP Space |
|
|
300 | (5) |
|
Phase III Identifying Blind and Confusing Traffic |
|
|
305 | (4) |
|
Phase IV Identifying Clients and Servers |
|
|
309 | (2) |
|
Identifying Sensing and Blocking Infrastructure |
|
|
311 | (1) |
|
Updating the Inventory: Toward Continuous Audit |
|
|
311 | (1) |
|
|
312 | (1) |
Index |
|
313 | |