27.1.5 lab convert data into a universal format

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Laboratorio 27.1.5
Nombre: Rafael Buenaño Semestre: 9no B
Lab - Convert Data into a Universal Format
Objectives
Part 1: Normalize Timestamps in a Log File
Part 2: Normalize Timestamps in an Apache Log File
Part 3: Log File Preparation in Security Onion Virtual Machine
Background / Scenario
This lab will prepare you to learn where log files are located and how to manipulate and view log files. Log
entries are generated by network devices, operating systems, applications, and various types of
programmable devices. A file containing a time-sequenced stream of log entries is called a log file.
By nature, log files record events that are relevant to the source. The syntax and format of data within log
messages are often defined by the application developer.
Therefore, the terminology used in the log entries often varies from source to source. For example, depending
on the source, the terms login, logon, authentication event, and user connection, may all appear in log entries
to describe a successful user authentication to a server.
It is often desirable to have a consistent and uniform terminology in logs generated by different sources. This
is especially true when all log files are being collected by a centralized point.
The term normalization refers to the process of converting parts of a message, in this case a log entry, to a
common format.
In this lab, you will use command line tools to manually normalize log entries. In Part 2, the timestamp field
will be normalized. In Part 3, the IPv6 field will be normalized.
Note: While numerous plugins exist to perform log normalization, it is important to understand the basics
behind the normalization process.
Required Resources
• CyberOps Workstation virtual machine
• Security Onion virtual machine
Instructions
Part 1: Normalize Timestamps in a Log File
Timestamps are used in log entries to specify when the recorded event took place. While it is best practice to
record timestamps in UTC, the format of the timestamp varies from log source to log source. There are two
common timestamp formats, known as Unix Epoch and Human Readable.
Unix Epoch timestamps record time by measuring the number of seconds that have passed since January
1,,
1970.
Human Readable timestamps record time by representing separate values for year, month, day, hour, minute,
and second.

The Human Readable Wed, 28 Jun 2017 13:27:19 GMT timestamp is the same as 1498656439 in Unix
Epoch.
From a programmability standpoint, it is much easier to work with Epoch as it allows for easier addition and
subtraction operations. From an analysis perspective; however, Human Readable timestamps are much
easier to interpret.
Converting Epoch to Human Readable Timestamps with AWK
AWK is a programming language designed to manipulate text files. It is very powerful and especially useful
when handling text files where the lines contain multiple fields, separated by a delimiter character. Log files
contain one entry per line and are formatted as delimiter-separated fields, making AWK a great tool for
normalizing.
Consider the applicationX_in_epoch.log file below. The source of the log file is not relevant.
2|Z|1219071600|AF|0
3|N|1219158000|AF|89
4|N|1220799600|AS|12
1|Z|1220886000|AS|67
5|N|1220972400|EU|23
6|R|1221058800|OC|89
The log file above was generated by what we will call application X. The relevant aspects of the file are:
o The columns are separated, or delimited, by the | character. Therefore, the data has five columns.
o The third column contains timestamps in Unix Epoch.
o The file has an extra line at the end. This will be important later in the lab.
Assume that a log analyst needs to convert the timestamps to a human-readable format. Follow the steps
below to use AWK to easily perform the manual conversion:
a. Launch the CyberOps Workstation VM and then launch a terminal window.
b. Use the cd command to change to the /home/analyst/lab.support.files/ directory. A copy of the file
shown above is stored there.
[analyst@secOps ~]$ cd /home/analyst/lab.support.files/
[analyst@secOps lab.support.files]$ ls -l
total 580
-rw-r--r-- 1 analyst analyst 649 Jun 28 18:34 apache_in_epoch.log
-rw-r--r-- 1 analyst analyst 126 Jun 28 11:13 applicationX_in_epoch.log
drwxr-xr-x 4 analyst analyst 4096 Aug 7 15:29 attack_scripts
-rw-r--r-- 1 analyst analyst 102 Jul 20 09:37 confidential.txt
<output omitted>
[analyst@secOps lab.support.files]$
c. Issue the following AWK command to convert and print the result on the terminal:
Note: Up arrow can be used to edit the typing errors in the previous command entry.
[analyst@secOps lab.support.files]$ awk 'BEGIN {FS=OFS="|"}
{$3=strftime("%c",$3)} {print}' applicationX_in_epoch.log
2|Z|Mon 18 Aug 2008 11:00:00 AM EDT|AF|0
3|N|Tue 19 Aug 2008 11:00:00 AM EDT|AF|89
4|N|Sun 07 Sep 2008 11:00:00 AM EDT|AS|12
1|Z|Mon 08 Sep 2008 11:00:00 AM EDT|AS|67
5|N|Tue 09 Sep 2008 11:00:00 AM EDT|EU|23

6|R|Wed 10 Sep 2008 11:00:00 AM EDT|OC|89
||Wed 31 Dec 1969 07:00:00 PM EST
The command above is an AWK script. It may seem complicated. The main structure of the AWK script
above is as follows:
• awk – This invokes the AWK interpreter.
• ‘BEGIN – This defines the beginning of the script.
• {} – This defines actions to be taken in each line of the input text file. An AWK script can have
several actions.
• FS = OFS = “|” – This defines the field separator (i.e., delimiter) as the bar (|) symbol. Different
text files may use different delimiting characters to separate fields. This operator allows the user
to define what character is used as the field separator in the current text file.
• $3 – This refers to the value in the third column of the current line. In the
applicationX_in_epoch.log, the third column contains the timestamp in epoch to be converted.
• strftime - This is an AWK internal function designed to work with time. The %c and $3 in between
parenthesis are the parameters passed to strftime.
• applicationX_in_epoch.log – This is the input text file to be loaded and used. Because you are
already in the lab.support.files directory, you do not need to add path information,
/home/analyst/lab.support.files/applicationX_in_epoch.log.
The first script action that defined in the first set of curly brackets is to define the field separator character
as the “|”. Then, in the second set of curly brackets, it rewrites the third column of each line with the result
of the execution of the strftime() function. strftime() is an internal AWK function created to handle time
conversion. Notice that the script tells the function to use the contents of the third column of each line
before the change ($3) and to format the output (%c).
Questions:
Were the Unix Epoch timestamps converted to Human Readable format? Were the other fields modified?
Explain.
Sí, el guión se convirtió de Epoch a Human Readable. La secuencia de
comandos cambió solo el campo de marca de tiempo, conservando el
resto del archivo
Compare the contents of the file and the printed output. Why is there the line, ||Wed 31 Dec 1969
07:00:00 PM EST?
El motivo de la línea adicional es porque el archivo tiene una línea vacía al
final, lo que llevó al script a interpretarlo por error como 0 y convertirlo en
una marca de tiempo legible por humanos.
Al interpretar la línea vacía como 0, el script convirtió 0 Unix Epoch a
Human Readable. 0 Unix Epoch se traduce a 0 segundos después de la
medianoche del 1 de enero de 1970. El guión muestra “Wed 31 Dec 1969
07:00:00 PM EST” porque se ajusta automáticamente a la zona
horaria. Debido a que CyberOps Workstation está configurada para EST
(UTC -5), el script muestra la medianoche del 1 de enero de 1970 menos 5
horas..
d. Use nano (or your favorite text editor) to remove the extra empty line at the end of the file and run the
AWK script again by using the up-arrow to find it in the command history buffer.
[analyst@secOps lab.support.files]$ nano applicationX_in_epoch.log

Question:
Is the output correct now? Explain.
Si. Debido a que se eliminó la línea vacía, el script no creó ni agregó datos
adicionales al archivo de registro.
e. While printing the result on the screen is useful for troubleshooting the script, analysts will likely need to
save the output in a text file. Redirect the output of the script above to a file named
applicationX_in_human.log to save it to a file:
[analyst@secOps lab.support.files]$ awk 'BEGIN {FS=OFS="|"}
{$3=strftime("%c",$3)} {print}' applicationX_in_epoch.log >
applicationX_in_human.log
Question:
What was printed by the command above? Is this expected?
No se imprimió nada en la pantalla. Sí, se espera, ya que la salida del
comando se redirigió a un archivo de texto llamado applicationX_in_human.log
f. Use cat to view the applicationX_in_human.log. Notice that the extra line is now removed and the
timestamps for the log entries have been converted to human readable format.
[analyst@secOps lab.support.files]$ cat applicationX_in_human.log
2|Z|Mon 18 Aug 2008 11:00:00 AM EDT|AF|0
3|N|Tue 19 Aug 2008 11:00:00 AM EDT|AF|89
4|N|Sun 07 Sep 2008 11:00:00 AM EDT|AS|12
1|Z|Mon 08 Sep 2008 11:00:00 AM EDT|AS|67
5|N|Tue 09 Sep 2008 11:00:00 AM EDT|EU|23
6|R|Wed 10 Sep 2008 11:00:00 AM EDT|OC|89
Part 2: Normalize Timestamps in an Apache Log File
Similar to what was done with the applicationX_in_epoch.log file, Apache web server log files can also be
normalized. Follow the steps below to convert Unix Epoch to Human Readable timestamps. Consider the
following Apache log file, apache_in_epoch.log:
[analyst@secOps lab.support.files]$ cat apache_in_epoch.log
198.51.100.213 - - [1219071600] "GET
/twiki/bin/edit/Main/Double_bounce_sender?topicparent=Main.ConfigurationVariables
HTTP/1.1" 401 12846
198.51.100.213 - - [1219158000] "GET
/twiki/bin/rdiff/TWiki/NewUserTemplate?rev1=1.3&rev2=1.2 HTTP/1.1" 200 4523
198.51.100.213 - - [1220799600] "GET /mailman/listinfo/hsdivision HTTP/1.1" 200 6291
198.51.100.213 - - [1220886000] "GET /twiki/bin/view/TWiki/WikiSyntax HTTP/1.1" 200
7352
198.51.100.213 - - [1220972400] "GET /twiki/bin/view/Main/DCCAndPostFix HTTP/1.1" 200
5253
198.51.100.213 - - [1221058800] "GET
/twiki/bin/oops/TWiki/AppendixFileSystem?template=oopsmore&m1=1.12&m2=1.12 HTTP/1.1"
200 11382
The Apache Log file above contains six entries which record events related to the Apache web server. Each
entry has seven fields. The fields are delimited by a space:
• The first column contains the IPv4 address, 198.51.100.213, of the web client placing the request.
• The second and third columns are not used and a “-“ character is used to represent no value.

• The fourth column contains the timestamp in Unix Epoch time, for example [1219071600].
• Type your answers here.
• The fifth column contains text with details about the event, including URLs and web request parameters.
All six entries are HTTP GET messages. Because these messages include spaces, the entire field is
enclosed with quotes.
• The sixth column contains the HTTP status code, for example 401.
• The seventh column contains the size of the response to the client (in bytes), for example 12846.
As in Part 1, a script will be created to convert the timestamp from Epoch to Human Readable.
a. First, answer the questions below. They are crucial for the construction of the script.
Questions:
In the context of timestamp conversion, what character would work as a good delimiter character for the
Apache log file above?
El character especial
How many columns does the Apache log file above contain?
7
In the Apache log file above, what column contains the Unix Epoch Timestamp?
Columna 4
b. In the CyberOps Workstation VM terminal, a copy of the Apache log file, apache_in_epoch.log, is stored
in the /home/analyst/lab.support.files.
c. Use an awk script to convert the timestamp field to a human readable format. Notice that the command
contains the same script used previously, but with a few adjustments for the delimiter, timestamp field,
and file name.
[analyst@secOps lab.support.files]$ awk 'BEGIN {FS=OFS=" "}
{$4=strftime("%c",$4)} {print}' apache_in_epoch.log
Question:
Was the script able to properly convert the timestamps? Describe the output.
No. Todas las marcas de tiempo son ahora el miércoles 31 de diciembre de
1969 a las 07:00:00 p.m. EST.
d. Before moving forward, think about the output of the script.
Question:
Can you guess what caused the incorrect output? Is the script incorrect? What are the relevant
differences between the applicationX_in_epoch.log and apache_in_epoch.log?
El problema son los corchetes en el archivo del curso. El script espera que
la marca de tiempo esté en el formato Unix Epoch que no incluye los
corchetes. Debido a que el script no sabe qué número representa el
carácter “[“, asume cero y devuelve el comienzo del tiempo de Unix en UTC
-5.
e. To fix the problem, the square brackets must be removed from the timestamp field before the conversion
takes place. Adjust the script by adding two actions before the conversion, as shown below:
{gsub(/[|]/,"",$4)}{print}{$4=strftime("%c",$4)}{print}'
apache_in_epoch.log
Notice after specifying space as the delimiter with {FS=OFS=” “}, there is a regular expression action to
match and replace the square brackets with an empty string, effectively removing the square brackets

that appear in the timestamp field. The second action prints the updated line so the conversion action can
be performed.
• gsub() – This is an internal AWK function used to locate and substitute strings. In the script
above, gsub() received three comma-separated parameters, described below.
• /[|]/ – This is a regular expression passed to gsub() as the first parameter. The regular
expression should be read as ‘find “[“ OR “]”’. Below is the breakdown of the expression:
o The first and last “/” character marks the beginning and end of the search block. Anything
between the first “/” and the second “/” are related to the search. The “” character is used
to escape the following “[“. Escaping is necessary because “[“ can also be used by an
operator in regular expressions. By escaping the “[“ with a leading “”, we tell the
interpreter that the “]” is part of the content and not an operator. The “|” character is the
OR operator. Notice that the “|” is not escaped and will therefore, be seen as an operator.
Lastly, the regular expression escapes the closing square bracket with “]”, as done
before.
• "" – This represents no characters, or an empty string. This parameter tells gsub() what to
replace the “[“ and “]” with, when found. By replacing the “[“ and “]” with “”, gsub() effectively
removes the “[“ and “]” characters.
• $4 – This tells gsub() to work only on the fourth column of the current line, the timestamp column.
Note: Regular expression interpretation is a SECOPS exam topic. Regular expressions are covered in
more detail in another lab in this chapter. However, you may wish to search the Internet for tutorials.
f. In a CyberOps Workstation VM terminal, execute the adjusted script, as follows:
{gsub(/[|]/,"",$4)}{print}{$4=strftime("%c",$4)}{print}'
apache_in_epoch.log
Question:
Was the script able to properly convert the timestamps this time? Describe the output.
Si. La salida ahora muestra dos líneas para cada entrada de registro. La
primera línea muestra la marca de tiempo en formato Unix Epoch y la
segunda línea es la misma entrada de registro con la marca de tiempo
mostrada en formato de lectura humana..
g. Shut down CyberOps Workstation VM if desired.
Part 3: Log File Preparation in Security Onion Virtual Machine
Because log file normalization is important, log analysis tools often include log normalization features. Tools
that do not include such features often rely on plugins for log normalization and preparation. The goal of these
plugins is to allow log analysis tools to normalize and prepare the received log files for tool consumption.
The Security Onion appliance relies on a number of tools to provide log analysis services. ELK, Zeek, Snort
and SGUIL are arguably the most used tools.
ELK (Elasticsearch, Logstash, and Kibana) is a solution to achieve the following:
• Normalize, store, and index logs at unlimited volumes and rates.
• Provide a simple and clean search interface and API.
• Provide an infrastructure for alerting, reporting and sharing logs.
• Plugin system for taking actions with logs.
• Exist as a completely free and open-source project.

Zeek (formerly called Bro) is a framework designed to analyze network traffic passively and generate event
logs based on it. Upon network traffic analysis, Zeek creates logs describing events such as the following:
• TCP/UDP/ICMP network connections
• DNS activity
• FTP activity
• HTTPS requests and replies
• SSL/TLS handshakes
Snort and SGUIL
Snort is an IDS that relies on pre-defined rules to flag potentially harmful traffic. Snort looks into all portions of
network packets (headers and payload), looking for patterns defined in its rules. When found, Snort takes the
action defined in the same rule.
SGUIL provides a graphical interface for Snort logs and alerts, allowing a security analyst to pivot from SGUIL
into other tools for more information. For example, if a potentially malicious packet is sent to the organization
web server and Snort raised an alert about it, SGUIL will list that alert. The analyst can then right-click that
alert to search the ELSA or Bro databases for a better understanding of the event.
Note: The directory listing maybe different than the sample output shown below.
Step 1: Start Security Onion VM.
Launch the Security Onion VM from VirtualBox’s Dashboard (username: analyst / password: cyberops).
Step 2: Zeek Logs in Security Onion
a. Open a terminal window in the Security Onion VM. Right-click the Desktop. In the pop-up menu, select
Open Terminal.
b. Zeek logs are stored at /nsm/bro/logs/. As usual with Linux systems, log files are rotated based on the
date, renamed and stored on the disk. The current log files can be found under the current directory.
From the terminal window, change directory using the following command.
analyst@SecOnion:~$ cd /nsm/bro/logs/current
analyst@SecOnion:/nsm/logs/current$
c. Use the ls -l command to see the log files generated by Zeek:
Note: Depends on the state of the virtual machine, there may not be any log files yet.
Step 3: Snort Logs
a. Snort logs can be found at /nsm/sensor_data/. Change directory as follows.
analyst@SecOnion:/nsm/bro/logs/current$ cd /nsm/sensor_data
analyst@SecOnion:/nsm/sensor_data$
b. Use the ls -l command to see all the log files generated by Snort.
analyst@SecOnion:/nsm/sensor_data$ ls -l
total 12
drwxrwxr-x 7 sguil sguil 4096 Jun 19 18:09 seconion-eth0
drwxrwxr-x 5 sguil sguil 4096 Jun 19 18:09 seconion-eth1
drwxrwxr-x 7 sguil sguil 4096 Jun 19 18:32 seconion-import
c. Notice that Security Onion separates files based on the interface. Because the Security Onion VM
image has two interfaces configured as sensors and a special folder for imported data, three directories
are kept. Use the ls –l seconion-eth0 command to see the files generated by the eth0 interface.

analyst@SecOnion:/nsm/sensor_data$ ls -l seconion-eth0
total 28
drwxrwxr-x 2 sguil sguil 4096 Jun 19 18:09 argus
drwxrwxr-x 3 sguil sguil 4096 Jun 19 18:09 dailylogs
drwxrwxr-x 2 sguil sguil 4096 Jun 19 18:09 portscans
drwxrwxr-x 2 sguil sguil 4096 Jun 19 18:09 sancp
drwxr-xr-x 2 sguil sguil 4096 Jun 19 18:24 snort-1
-rw-r--r-- 1 sguil sguil 5594 Jun 19 18:31 snort-1.stats
-rw-r--r-- 1 root root 0 Jun 19 18:09 snort.stats
Step 4: Various Logs
a. While the /nsm/ directory stores some log files, more specific log files can be found under /var/log/nsm/.
Change directory and use the ls command to see all the log files in the directory.
analyst@SecOnion:/nsm/sensor_data$ cd /var/log/nsm/
analyst@SecOnion:/var/log/nsm$ ls
eth0-packets.log sid_changes.log
netsniff-sync.log so-elastic-configure-kibana-dashboards.log
ossec_agent.log so-elasticsearch-pipelines.log
pulledpork.log so-sensor-backup-config.log
seconion-eth0 so-server-backup-config.log
seconion-import sosetup.log
securityonion so-zeek-cron.log
sensor-clean.log squert-ip2c-5min.log
sensor-clean.log.1.gz squert-ip2c.log
sensor-clean.log.2.gz squert_update.log
sensor-newday-argus.log watchdog.log
sensor-newday-http-agent.log watchdog.log.1.gz
sensor-newday-pcap.log watchdog.log.2.gz
sguil-db-purge.log
Notice that the directory shown above also contains logs used by secondary tools such as OSSEC and
Squert.
b. ELK logs can be found in the /var/log directory. Change directory and use the ls command to list the files
and directories.
analyst@SecOnion:/var/log/nsm$ cd ..
analyst@SecOnion:/var/log$ ls
alternatives.log debug kern.log.1 samba
alternatives.log.1 debug.1 kern.log.2.gz sguild
apache2 debug.2.gz kibana so-boot.log
apt dmesg lastlog syslog
auth.log domain_stats lightdm syslog.1
auth.log.1 dpkg.log logstash syslog.2.gz
auth.log.2.gz dpkg.log.1 lpr.log syslog.3.gz
boot elastalert mail.err syslog.4.gz
boot.log elasticsearch mail.info unattended-upgrades
bootstrap.log error mail.log user.log
btmp error.1 mail.warn user.log.1
btmp.1 error.2.gz messages user.log.2.gz
cron.log faillog messages.1 wtmp

cron.log.1 freq_server messages.2.gz wtmp.1
cron.log.2.gz freq_server_dns mysql Xorg.0.log
curator fsck nsm Xorg.0.log.old
daemon.log gpu-manager.log ntpstats
daemon.log.1 installer redis
daemon.log.2.gz kern.log salt
c. Take some time to Google these secondary tools and answer the questions below:
Question:
For each one of the tools listed above, describe the function, importance, and placement in the security
analyst workflow.
Sphinx es un motor de búsqueda de código abierto y ELSA lo utiliza para proporcionar
capacidades de búsqueda.
Pulledpork es un sistema de gestión de reglas de Snort. Facilita la actualización de las
reglas de Snort. Las reglas desactualizadas de Snort hacen que todo el sistema sea
inútil.
OSSEC es un sistema que se utiliza para normalizar y concentrar los registros del
sistema local. Cuando se implementa en toda la organización, OSSEC permite que un
analista tenga una idea clara de lo que está sucediendo en los sistemas.
Squert es una herramienta visual que intenta proporcionar contexto adicional a los
eventos mediante el uso de metadatos, representaciones de series de tiempo y
conjuntos de resultados ponderados y agrupados lógicamente
Reflection
Log normalization is important and depends on the deployed environment.
Popular tools include their own normalization features, but log normalization can also be done manually.
When manually normalizing and preparing log files, double-check scripts to ensure the desired result is
achieved. A poorly written normalization script may modify the data, directly impacting the analyst’s work.
End of document

27.1.5 lab convert data into a universal format

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