27.1.5 Lab – Convert Data into a Universal Format (Instructor Version)
Instructor Note: Red font color or gray highlights indicate text that appears in the instructor copy only.
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:
- The columns are separated, or delimited, by the | character. 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:
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",$3)} {print}' applicationX_in_epoch.log
2|Z|Mon 18 Aug 2008 11:00:00 AM EDT|AF|0
3|N|Tue 19 Aug 2008 11:00:00 AM EDT|AF|89
4|N|Sun 07 Sep 2008 11:00:00 AM EDT|AS|12
1|Z|Mon 08 Sep 2008 11:00:00 AM EDT|AS|67
5|N|Tue 09 Sep 2008 11:00:00 AM EDT|EU|23
6|R|Wed 10 Sep 2008 11:00:00 AM EDT|OC|89
||Wed 31 Dec 1969 07:00:00 PM EST
[analyst@secOps lab.support.files]$
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 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 ($3) and to format the output (%c).
Were the Unix Epoch timestamps converted to Human Readable format? Were the other fields modified? Explain.
Compare the contents of the file and the printed output. Why is there the line, ||Wed 31 Dec 1969 07:00:00 PM EST?
d. Use nano (or your favorite text editor) to remove the extra empty line at the end of the file and run the AWK script again by using the up-arrow to find it in the command history buffer.
[analyst@secOps lab.support.files]$ nano applicationX_in_epoch.log
Is the output correct now? Explain.
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",$3)} {print}' applicationX_in_epoch.log > applicationX_in_human.log
[analyst@secOps lab.support.files]$
What was printed by the command above? Is this expected?
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 [analyst@secOps lab.support.files]$
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, 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.
a. 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?
How many columns does the Apache log file above contain?
In the Apache log file above, what column contains the Unix Epoch Timestamp?
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",$4)} {print}' apache_in_epoch.log
Was the script able to properly convert the timestamps? Describe the output.
d. Before moving forward, think about the output of the script.
Can you guess what caused the incorrect output? Is the script incorrect? What are the relevant differences between the applicationX_in_epoch.log and apache_in_epoch.log?
e. To fix the problem, the square brackets must be removed from the timestamp field before the conversion takes place. Adjust the script by adding two actions before the conversion, as shown below:
[analyst@secOps lab.support.files]$ 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.
- $4 – This tells gsub() to work only on the fourth column of the current line, the timestamp column.
Note: Regular expression interpretation is a SECOPS exam topic. Regular expressions are covered in more detail in another lab in this chapter. However, you may wish to search the Internet for tutorials.
f. In a CyberOps Workstation VM terminal, execute the adjusted script, as follows:
[analyst@secOps lab.support.files]$ 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.
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
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 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:
For each one of the tools listed above, describe the function, importance, and placement in the security analyst workflow.
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.
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.
