CISCO CyberOps lab – Convert Data into a Universal Format; cybersecurity training


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


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.








The log file above was generated by what we will call application X. The relevant aspects of the file are:

  • The columns are separated, or delimited, by the | Therefore, the data has five columns.
  • The third column contains timestamps in Unix Epoch.
  • 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:

  1. Launch the CyberOps Workstation VM and then launch a terminal window.
  2. Use the cd command to change to the /home/analyst/ A copy of the file shown above is stored there.

[analyst@secOps ~]$ cd /home/analyst/

[analyst@secOps]$ 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>


  1. 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]$ 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 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.
  • log – This is the input text file to be loaded and used. Because you are already in the directory, you do not need to add path information, /home/analyst/
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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).


Were the Unix Epoch timestamps converted to Human Readable format? Were the other fields modified? Explain.

Type your answers here.

Yes, the script converted from Epoch to Human Readable. The script changed only the timestamp field, preserving the rest of the file.

Compare the contents of the file and the printed output. Why is there the line, ||Wed 31 Dec 1969 07:00:00 PM EST?

Type your answers here.

The reason for the extra line is because the file has an empty line at the end, which led the script to mistakenly interpret it as 0 and convert that into a Human Readable timestamp.

By interpreting the empty line as 0, the script converted 0 Unix Epoch to Human Readable. 0 Unix Epoch translates to 0 seconds after midnight of Jan 1st, 1970. The script displays “Wed 31 Dec 1969 07:00:00 PM EST” because it automatically adjusts for the timezone. Because the CyberOps Workstation is configured for EST (UTC -5), the script displays midnight, Jan 1, 1970 minus 5 hours.

  1. 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]$ nano applicationX_in_epoch.log


Is the output correct now? Explain.

Type your answers here.

Yes. Because the empty line was removed, no extra data was created and added to the log file by the script.

  1. 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 log to save it to a file:

[analyst@secOps]$ awk ‘BEGIN {FS=OFS=”|”} {$3=strftime(“%c”,$3)} {print}’ applicationX_in_epoch.log > applicationX_in_human.log



What was printed by the command above? Is this expected?

Type your answers here.

Nothing was printed on the screen. Yes, it is expected, as the command output was redirected to a text file named applicationX_in_human.log.

  1. Use cat to view the 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]$ 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]$ cat apache_in_epoch.log – – [1219071600] “GET /twiki/bin/edit/Main/Double_bounce_sender?topicparent=Main.ConfigurationVariables HTTP/1.1” 401 12846 – – [1219158000] “GET /twiki/bin/rdiff/TWiki/NewUserTemplate?rev1=1.3&rev2=1.2 HTTP/1.1” 200 4523 – – [1220799600] “GET /mailman/listinfo/hsdivision HTTP/1.1” 200 6291 – – [1220886000] “GET /twiki/bin/view/TWiki/WikiSyntax HTTP/1.1” 200 7352 – – [1220972400] “GET /twiki/bin/view/Main/DCCAndPostFix HTTP/1.1” 200 5253 – – [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, 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].
  • 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.

  1. First, answer the questions below. They are crucial for the construction of the script.


In the context of timestamp conversion, what character would work as a good delimiter character for the Apache log file above?

Type your answers here.

The space character

How many columns does the Apache log file above contain?

Type your answers here.


In the Apache log file above, what column contains the Unix Epoch Timestamp?

Type your answers here.

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Column 4

  1. In the CyberOps Workstation VM terminal, a copy of the Apache log file, apache_in_epoch.log, is stored in the /home/analyst/
  2. 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]$ awk ‘BEGIN {FS=OFS=” “} {$4=strftime(“%c”,$4)} {print}’ apache_in_epoch.log


Was the script able to properly convert the timestamps? Describe the output.

Type your answers here.

No. All timestamps are now Wed 31 Dec 1969 07:00:00 PM EST.

  1. Before moving forward, think about the output of the script.


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?

Type your answers here.

The problem is the square brackets in the course file. The script expects the timestamp to be in the Unix Epoch format which does not include the square brackets. Because the script does not know what number represents the “[“ character, it assumes zero and returns the Unix beginning of time in UTC -5.

  1. 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:

[analyst@secOps]$ awk ‘BEGIN {FS=OFS=” “} {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:
    • 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.

  1. In a CyberOps Workstation VM terminal, execute the adjusted script, as follows:

[analyst@secOps]$ awk ‘BEGIN {FS=OFS=” “} {gsub(/\[|\]/,””,$4)}{print}{$4=strftime(“%c”,$4)}{print}’ apache_in_epoch.log


Was the script able to properly convert the timestamps this time? Describe the output.

Type your answers here.

Yes. The output now displays two lines for each log entry. The first line displays the timestamp in Unix Epoch format and the second line is the same log entry with the timestamp displayed using Human Readable format.

  1. 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.

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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

  1. Open a terminal window in the Security Onion VM. Right-click the Desktop. In the pop-up menu, select Open Terminal.
  2. 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 From the terminal window, change directory using the following command.

analyst@SecOnion:~$ cd /nsm/bro/logs/current


  1. 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

  1. Snort logs can be found at /nsm/sensor_data/. Change directory as follows.

analyst@SecOnion:/nsm/bro/logs/current$ cd /nsm/sensor_data


  1. 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

  1. 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

  1. 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


Notice that the directory shown above also contains logs used by secondary tools such as OSSEC and Squert.

  1. ELK logs can be found in the /var/log 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      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

  1. Take some time to Google these secondary tools and answer the questions below:


For each one of the tools listed above, describe the function, importance, and placement in the security analyst workflow.

Type your answers here.

Pulledpork is a Snort rule manage system. It facilitates Snort rules updating. Outdated Snort rules makes the entire system useless.

OSSEC is a system used to normalize and concentrate local system logs. When deployed throughout the organization, OSSEC allows an analyst to have a clear picture of what is happening in the systems.

Squert is a visual tool that attempts to provide additional context to events through the use of metadata, time series representations, and weighted and logically grouped result sets.

Elasticsearch is a distributed search and analytics engine. The data is stored centrally to provide fast searches and allow for fine tuning.

Logstash gathers the data from different sources and feeds the data into ElasticSearch.

Kibana is the data visualization for the ELK stack to provide quick insight into the data.


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.

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