We only need to append

Introduction

As Elasticsearch matures over time, they are fixing some of the less obvious stuff. Seemingly little things can be tremendously important though.

One of the new things, that I want to highlight here is the new security privilegie : create_doc. Read about it the Elasticsearch 7.5 release notes.

As Elastic describes it:

With the previous set of index privileges, users that were allowed to index new documents were also allowed to update existing ones.

With the new create_doc, cluster administrators can create a user that is allowed to add new data only. This gives the minimum privileges needed by ingest agents, with no risk that that user can alter and corrupt existing logs. These administrators can (now) rest assured knowing that components that live directly on the machines that they monitor cannot corrupt or hide tracks that are already into the index.

Have a look at the documention as there is one important change, that is needed in the Elasticsearch Logstash output section.

Implementing it

It is very easy to take advantage of this new feature. Create a role called append_writer and assign a user to the new role:

Or if you prefer developer tools

The final to modify is the output section in Logstash. You need to add an action attribute to it:

Of course , the credentials of the append_writer should be kept in secret store of Logstash!

Conclusion

This simple change is trivial to make, but gives great value. You can rest assured, that the user used in Logstash can never be used to change existing documents in your Elastic clusters.

Using Logstash @metadata

Introduction

In a previous post, I showed how to do simple Kafka and Elasticsearch integration. It showed how to use a single Kafka topic to carry many different types of logs into Elasticsearch.

Have a read if you want to catch up or haven’t read it.

This approach had an undesired sideeffect of putting attributes into Elasticsearch, that are not needed and wasting precious diskspace.

Metadata

However there is very simple and elegant way to fix this. Have a read of the description of Logstash metadata fields here

Previous article suggested this approach. This meant storing kafkatopic,myapp and myrotation in every single document, that went through pipeline.

filter {
    mutate {
        copy => { "[@metadata][kafka][topic]" => "kafkatopic" }
    }

   if ![myapp]
   {
     mutate {
       add_field => { "myapp" => "default" }
     }
   }

   if ![myrotation]
   {
     mutate {
       add_field => { "myrotation" => "weekly" }
     }
   }
}

If we convert to using metadata fields, it could look like this instead. No more kafkatopic,myapp or myrotation being stored.

filter {

   if ![myapp]
   {
     mutate {
       add_field => { "myapp" => "default" }
     }
   }

   if ![myrotation]
   {
     mutate {
       add_field => { "myrotation" => "weekly" }
     }
   }
   # take advantage of metadata fields
   if [myapp]
   {
      mutate {
        rename => { "myapp" => "[@metadata][myapp]" }
      }
   }
   if [myrotation]
   {
      mutate {
        rename => { "myrotation" => "[@metadata][myrotation]" }
      }
   }
}

We can then use the new metadata stuff in the output section

output
{
      if [@metadata][myrotation] == "rollover"
      {
                  elasticsearch {
                                   hosts => ["https://elastic01:9200" , "https://elastic02:9200"]
                                   manage_template => false
                                   index => "%{[@metadata][kafka][topic]}-%{[@metadata][myapp]}-active"
                   }
      }

      if [@metadata][myrotation] == "daily"
      {
                   elasticsearch {

                                   hosts => ["https://elastic01:9200" , "https://elastic02:9200"]
                                   manage_template => false
                                   index => "%{[@metadata][kafka][topic]}-%{[@metadata][myapp]}-%{+YYYY.MM.dd}"
                   }
     }

      if [@metadata][myrotation] == "weekly"
      {
                  elasticsearch {
                                   hosts => ["https://elastic91:9200" , "https://elastic02:9200"]
                                   manage_template => false
                                   index => "%{[@metadata][kafka][topic]}-%{[@metadata][customapp]}-%{+xxxx.ww}"
                   }
      }
  }

Debugging

As all outputs automatically remove the @metadata object and you are trying to debug your conf file, you now need to do a simple trick to display the contents of metadata.

output
{
  # also show contents of metadata object
  stdout { codec => rubydebug { metadata => true } }
}

Conclusion

So by using this approach we are no longer storing kafkatopic,myapp and myrotation as attributes in every single document, that is passing through this pipeline.

We save diskspace,processing time and documents are clean.

Simplifying Logstash by adding complexity

Background

A lot of logs that goes into Logstash will be done using the beats protocol. So you will have a pipeline in Logstash listening for beats on port 5044 typically. This could be stuff coming from filebeat,winlogbeat,metricbeat or heartbeat.

In your Logstash filter section, you will over time end up with a huge mess trying to add the relevant parsing of logs inside a bunch of if statements. In the output section, you could see the same mess again,where you output the different types of logs inside another bunch of if statements.

If you have done stuff like this, your code will be increasingly difficult to read and debug. Not to mention the problems, you will face, if multiple persons need to be able to contribute to the configuration of Logstash. Also if you need to move parsing of a specific type to another Logstash node. Then you need to grab the relevant parts by copy/paste, which is errorprone.

input {
  beats {
    port => 5044
  }
}

filter {
  if [type] =="winlogbeat" {
    #enrich winlogbeat
    ....
  }
  if [type] =="heartbeat" {
    #enrich heartbeat
    ....
  }
  if [type] =="mylogfile" {
    #enrich mylogfile
    ....
  }
  if [type] =="dns" {
    #enrich dns
    ....
  }
  if [type] =="dhcp" {
    #enrich ddhcp
    ....
  }
}

output {
  if [type] =="winlogbeat" {
    #output winlogbeat
    ....
  }
  if [type] =="heartbeat" {
    #output heartbeat
    ....
  }
  if [type] =="mylogfile" {
    #output mylogfile
    ....
  }
  if [type] =="dns" {
    #output dns
    ....
  }
  if [type] =="dhcp" {
    #output dhcp
    ....
  }
}

Simplifying

So what to do about this problem you may ask. Earlier people did some stuff by using named conf files that would be picked up by Logstash to form a large configuration. However we want to be modern and use new features made available by Elastic.

Pipeline to pipeline

I read about pipeline-to-pipeline feature in Logstash a long time ago. There is an excellent article about the options here. This feature is now generally available in 7.4.

It’s actually very simple to implement. You create a pipeline file to receive the beats input and then distribute the events to small tailor made pipelines.

input {
  beats {
    port => 5044
  }
}

filter {
}

output {
        if [type] == "dns" {
          pipeline { send_to => dns }
        } else if [type] == "dhcp" {
          pipeline { send_to => dhcp }
        } else if [type] == "mylogfile" {
          pipeline { send_to => mylogfile }
        } else {
          pipeline { send_to => fallback }
        }
}

Then create a new pipeline to handle the specific log type. This code is restricted to parsing DNS logs.

input {
  pipeline { address => dns }
}

filter {
   # do only your parsing of DNS logs
}

output {
  # output dns
}

You must remember to add all your pipelines to your pipelines.yml file. Remember to think about whether you need in-memory queue or persisted queue per pipeline.

- pipeline.id: beats-input
  path.config: "/etc/path/to/beats-input.config"
  pipeline.workers: 3
- pipeline.id: dns
  path.config: "/etc/different/path/dns.cfg"
  queue.type: persisted
  queue.max_bytes: 4gb
- pipeline.id: dhcp
  path.config: "/etc/different/path/dhcp.cfg"
  queue.type: persisted
  queue.max_bytes: 1gb
- pipeline.id: mylogfile
  path.config: "/etc/different/path/mylogfile.cfg"
  queue.type: persisted
  queue.max_bytes: 2gb

Conclusion

We have started using this approach and will be doing this going forward. We get a much simpler way of handling many different types inside Logstash and we are able to distribute the work to more people.

On top of this we are seeing better latency times in logstash. I suggest to read this article while you are at it. You are effectively using parallel pipelines like the article suggests by this approach.

As always, use this approach if you find it applicable to your usecase.

Parsing Linux DHCP logs

Introduction

So we are back at parsing DHCP logs. This time , we are taking a look at DHCP logs from Linux systems. They are quite simple and easy to parse. So while this is easy, let’s add some complication to the puzzle. Namely ECS.

ECS

Elastic Common Schema. This is a fairly new convention introduced by Elastic to help out with naming fields in your logs. The main goal is to achieve a consistent naming through your various log sources. This enables your users to know , that a IP adress is always parsed as [source][ip] and not src_ip,src_src_ipv4 and so on.

If you follow ECS , you will be able to index various kinds of firewall in the same Index and be able to visualize the data in Kibana, regardless of whether the data comes from Check Point , ASA or whatever.

The main problem with ECS is that it doesnt cover all sorts of naming yet. So you will find yourself in situation , where you simply can’t find something in ECS, that covers your particular usecase. But dont worry about it, just use what you can and be consistent.

DHCP messages

In your Linux DHCP logs, you should find lines that looks similar to this:

Apr 29 03:48:58 hostname dhcpd: DHCPACK to ip (mac) via router
Apr 29 03:48:58 hostname dhcpd: DHCPINFORM from ip via router
Apr 29 03:48:59 hostname dhcpd: DHCPDISCOVER from mac via router: unknown network segment
Apr 29 03:49:05 hsotname dhcpd: DHCPREQUEST for ip from mac (hostname) via router

So we will build a Logstash pipeline to parse this and convert it to ECS naming, whenever possible.

Logstash pipeline

We will jump right in there with some Logstash code. This pipeline will read input from Stdin, parse the logs and output the parsed to the console in a nice JSON format.

You will need Filebeat to ship your logs to Logstash and you will need to modify the pipeline to read a Beats input instead. You will also need to modify the Output section , so it will output to your desired location, whether it be Kafka or Elasticsearch directly.

input {

  stdin {
    id => "my_plugin_id"
  }
 
}

 

filter {

	if [message] =~ /dhcpd:/
	{
		grok{
			match => { 
				"message" => "%{SYSLOGBASE} %{GREEDYDATA:params}" 
					}
		}
		if "_grokparsefailure"	 not in [tags]
		{
			grok{
				match => { 
					"params" => "%{WORD:[event][action]} %{GREEDYDATA:params2}" 
						}
			}
			if "_grokparsefailure"	 not in [tags]
			{
				grok{
					match => { 
						"params2" => 
						[
							"from %{IP:[source][ip]} via %{IPORHOST:interface}" ,
							"to %{IP:[source][ip]} \(%{MAC:[source][mac]}\) via %{IPORHOST:interface}" ,
							"from %{MAC:[source][mac]} (\(%{DATA:[host[hostname]}\) )?via %{IPORHOST:interface}(: %{GREEDYDATA:[error][message]})?" ,
							"on %{IP:[source][ip]} to %{MAC:[source][mac]} \(%{DATA:[host[hostname]}\) via %{IPORHOST:interface}" ,
							"pool %{WORD:[pool][id]} %{DATA:[pool][subnet]}  total %{INT:[pool][total]}  free %{INT:[pool][free]}  backup %{INT:[pool][backup]}  lts %{INT:[pool][lts]}  max-own \(\+\/\-\)%{INT:[pool][maxown]}",
							"pool %{WORD:[pool][id]} %{DATA:[pool][subnet]}  total %{INT:[pool][total]}  free %{INT:[pool][free]}  backup %{INT:[pool][backup]}  lts %{INT:[pool][lts]}  max-misbal %{INT:[pool][maxmisbal]}"
						]
							}
				}
			}
		}
		if "_grokparsefailure"	 not in [tags]
		{
			if [source][mac]
			{
				mutate
				{
					gsub => [
					  # replace backslashes, question marks, hashes, and minuses
					  # with a dot "."
					  "[source][mac]", "[\:]", ""
					]
				}
			}
			if [source][ip]
			{
				mutate {
					   copy => { "[source][ip]" => "[related][ip]" }
				}
			}
		
		
			date {
				match => [ "timestamp",  "MMM  d HH:mm:ss", "MMM dd HH:mm:ss", "ISO8601" ]
			}
			mutate {
			    remove_field => [ "message", "params2", "params", "timestamp" ]
				add_field => { "[ecs][version]" => "ecs-1.0.0" }
				add_field => { "[organization][id]" => "xx" }
				add_field => { "[observer][vendor]" => "linux" }
				 rename => { "logsource" => "[observer][host]"  }
			}
			
		}
	
		
	}
  
}  
 

output {

  stdout { codec => rubydebug }
  
  
}

Github

In order to make our lifes easier, we have posted the code in our Github repo.

Conclusion

This was a short example of parsing another log into Elastic. I highly encourage you to get yourself familiar with ECS. It will make a lot of things easier and more shareable for everyone.

Scaleable syslog pipeline

If you are receiving syslog data from a variety of network devices, you need a design that will allow you to receive and process syslog messages before you ingest them into your Elasticsearch cluster.

Processing syslog messages can be quite heavy in terms of CPU usage, if you are doing a lot of grok statements.

As always, this can be done in many different ways, but in this blog post I will show the basics of a Kafka based architecture.

Initial approach, that will work in many usecases: just put some kind of loadbalancer in front and use that to receive your syslog messages and ship them to some Logstash instances for processing.

This approach will be fine for a small to medium sized setup. But how will you scale this approach? Well , deploy one more Logstash server and edit your loadbalancer configuration to use the new Logstash server. But there is a smarter way.

I suggest that you have your loadbalancer forwarding to 2-3 Logstash servers. You create an extremely simple syslog pipeline. In this syslog input pipeline do absolutely nothing but forward the data to your Kafka cluster.

input {
  tcp {
    port => 514
    type => syslog
  }
  udp {
    port => 514
    type => syslog
  }
}

filter {
}

output{
  kafka { 
    bootstrap_servers => "kafka-01"
    topic_id =>  "raw-syslog"
  }
}

Of course since this is syslog be sure, that this pipeline is backed by a persistent queue in Logstash as syslog is send and forget.

The boxes to run this pipeline can be quite small as there will be no processing going on.

If you are running with RSyslog, you could even configure the RSyslog to send directly to Kafka and you won’t need this Logstash input pipeline.

But right now, you have just raw syslog messages living in your Kafka cluster. You need to process them. They could be ASA firewall messages, where you need to parse them.

So you create an additional Logstash pipeline, that pulls the raw syslog data from Kafka and parses it. This pipeline should be running on other boxes than the one, that received the data. Preferably some quite beefy Logstash nodes. When parsed you can send it back to Kafka, if you need or you can ingest into your Elasticsearch cluster at this point.

kafka{
	group_id => "raw-syslog-group"
	topics => ["raw-syslog"]
	bootstrap_servers => "kafka-01:<port>"
	codec => json
}

filter {
    if "%ASA-" in [message] {
      grok {
        match => [
          "message", "<%{POSINT:syslog_pri}>%{CISCOTIMESTAMP:timestamp} %{SYSLOGHOST:sysloghost} %%{CISCOTAG:cisco_tag}: %{GREEDYDATA:cisco_message}"
        ]
      }
      syslog_pri { }
  ....
  ....
  }
}

elasticsearch {
 hosts => ["elastic-01:9200"]
 index => "syslog-%{+YYYY.MM.dd}" 
}

The trick is the group consumer feature of Kafka. In the example I specified group_id => “raw-syslog-group” . So no matter how many Logstash instances have this pipeline running, they will be working as a unit in regards to Kafka.

If you find you need more processing power , deploy an additional Logstash node and deploy this pipeline. You dont have to change your loadbalancer configuration at all.

This setup also makes your life easier , if you can centralize your Logstash processing to a few beefy Logstash nodes. Comes in handy if you are thinking of using Memcached for lookup of malicious IP’s or domain names in all your syslog messages. Hey , that sounds like a topic for a complete blog post of its own;)

Adding Windows DHCP logs to Elastic – part 2

If you followed the previous blog, we have created a pipeline for sending Windows Server DHCP logs into Elastic. But that was just the basic logs. Suppose we want to enrich the logs with the Mac Address Vendor Information. So you will be able to monitor what devices appear on your network. Please note that Mac Addresses can be manipulated by 3 party.

A Mac Address consists of 2 parts . You have the Mac Vendor Prefix of 3 bytes followed by a unique identifier of another 3 bytes.

On a Windows computer you have multiple ways to display the Mac Address. These include:

  • ipconfig /all
  • getmac
ipconfig /all
Ipconfig /all

In this example , we have a Mac Address of A0-B3-CC-85-30-F4. Our Vendor prefix will be A0-B3-CC. But how to translate this prefix into Vendor information?

Fortunately we can download a list of known Vendors http://standards-oui.ieee.org/oui.txt

So we download that list and look for our example prefix, we find this prefix as registered by HP.

Vencor a0-b3-cc
Vendor a0-b3.cc

To be able to use the downloaded OUI list in logstash we need to convert the file to a YML file. The file will also contain some characters that logstash don’t appreciate, so we will try to strip those characters also. We will use a small powershell script to do this conversion. The script contains hardcoded locations, which you need to update.

# assume we have downloaded script to this location
$file = "c:\source\oui.txt"
# target location
$dest = "c:\source\oui.yml"

# regex to filter lines that contains the "(base 16)" wording
$regex = "(^.{6})\s+\(base 16\)\s+(.*)$"

if (Test-path $dest)
{
    remove-item $dest
}

$streamWriter = [System.IO.StreamWriter] "$dest"
foreach($line in Get-Content $file) 
{
    if($line -match $regex)
   {
       $company = $matches[2]
       # strip out strange characters
       $nice = $company -replace "[^0-9a-zA-Z ]+" , ""
       # write to dest YML file
       $streamWriter.WriteLine("`"$($matches[1])`": $($nice)");
    }
}
# we are done
$streamWriter.close();

We are now able to expand our logstash filter, to use our new Mac Address vendor lookup information. We will do this with the help of the translate filter. This allows us to lookup a value in the YML file.

if [Description] == "Assign" or [Description] == "Renew"
{
   grok 
   {
    # split the mac address into 2 elements
    match => [ "MAC_Address", "(?<ouiprefix>.{6})(?<ouisuffix>.*)" ]
   }
   # lookup the ouiprefix in the YML file and add new oui field
   translate 
   {
     dictionary_path => "c:\elastic\logstash\config\oui.yml"
     field      => "[ouiprefix]"
     destination => "[oui]"
     fallback => "N/A"
   }
}

That’s all you need to do to enrich the DHCP logs with Mac Address Vendor. As usual we leave it to you to make a visualization of this info and add that to your DHCP dashboard.

Next part of this series will adress trying to find unusual hostnames in the DHCP logs. We will try to develop a pattern on the typical hostname and use Elastic Machine Learning to find uncommon patterns.

Adding Windows DHCP logs to Elastic – part 1

Prerequisites

In order to add Windows DHCP server logs to Elastic, we assume that you have the infrastructure needed.

  • Windows DHCP Server 2012 R2 or higher
  • Elasticsearch cluster
  • Logstash

We are going to work with Elastic 6.x in this setup.  

Filebeat

Install filebeat on your DHCP server in a directory of your liking.

The DHCP logs are located in %systemroot%\system32\dhcp\dhcpsrvlog*.log. You will also find ipv6 logs, but we will focus on ipv4 logs.

Add the following code to your filebeat.yml. This is the prospector, that is going to watch for your DHCP logs. Notice that I am adding a field called Type under root with the value dhcp. This is a field , we will using in the Logstash configuration.

    -
      # Paths that should be crawled and fetched. Glob based paths.
      # To fetch all ".log" files from a specific level of subdirectories
      # /var/log/*/*.log can be used.
      # For each file found under this path, a harvester is started.
      # Make sure not file is defined twice as this can lead to unexpected behaviour.
      paths:
        #- /var/log/*.log
        - c:\windows\system32\dhcp\DhcpSrvLog-*.log
      input_type: log
      include_lines: ["^[0-9]"]
      document_type: dhcp
      close_removed : false
      clean_removed : false                 
      ignore_older: 47h
      clean_inactive: 48h     
      fields:
        type: dhcp
      fields_under_root: true

And add an output section also. There are multiple ways of shipping data from Filebeat. In this example we are shipping the logs to Logstash for parsing. DNS names are logstash01 and logstash02.

output:

  ### Logstash as output
  logstash:
    # The Logstash hosts
    hosts: ["logstash01:5044" , "logstash:5044" ]

    # Number of workers per Logstash host.
    worker: 2

    # Set gzip compression level.
    #compression_level: 3

    # Optional load balance the events between the Logstash hosts
    loadbalance: true

    # Optional index name. The default index name depends on the each beat.
    # For Packetbeat, the default is set to packetbeat, for Topbeat
    # top topbeat and for Filebeat to filebeat.
    #index: filebeat

    # Optional TLS. By default is off.
    #tls:
      # List of root certificates for HTTPS server verifications
      #certificate_authorities: ["/etc/pki/root/ca.pem"]

      # Certificate for TLS client authentication
      #certificate: "/etc/pki/client/cert.pem"

      # Client Certificate Key
      #certificate_key: "/etc/pki/client/cert.key"

      # Controls whether the client verifies server certificates and host name.
      # If insecure is set to true, all server host names and certificates will be
      # accepted. In this mode TLS based connections are susceptible to
      # man-in-the-middle attacks. Use only for testing.
      #insecure: true

      # Configure cipher suites to be used for TLS connections
      #cipher_suites: []

      # Configure curve types for ECDHE based cipher suites
      #curve_types: []

After these steps, filebeat should be able to watch the DHCP server and ship them to Logstash.

Logstash

In order for logstash to process the data coming from your DHCP server , we create an input section and specify it as beats input.

input {
  beats {
   port => 5044
  }
}

Next we define the filter section, where we will parse the logs. Notice that we are using the dissect filter here. This is very convinient for this kind of stuff and you dont have to use grok for simple stuff like this.

filter
{
    if [type] == "dhcp"
    {
        dissect {
          mapping => {
          "message" => "%{ID},%{Date},%{Time},%{Description},%{IP_Address},%{Host_Name},%{MAC_Address},%{User_Name},%{TransactionID},%{QResult},%{Probationtime},%{CorrelationID},%{Dhcid},%{VendorClass_hex},%{VendorClass_ascii},%{UserClass_hex},%{UserClass_ascii},%{RelayAgentInformation},%{DnsRegError}"
                   }
           } 
         mutate
         {
            add_field => { "log_timestamp" => "%{Date}-%{Time}" }
         }
         date {
              match => [ "log_timestamp", "MM/dd/YY-HH:mm:ss" ]                
              timezone => "Europe/Copenhagen"
         }
         if "_dateparsefailure" not in [tags]
         {
             mutate
             {
                remove_field=> ['Date', 'Time', 'log_timestamp', 'message']
             }
        }
     }
}

And finally we define the output section, where we ship data from Logstash to Elastic. We are using a daily index in this example, but you could use a weekly or even monthly approach instead as there will not be a huge amount of data in this index.

output {
  if [type] == "dhcp"
  {
    elasticsearch {
      hosts => ["http://localhost:9200"]
      index => "dchp-%{+YYYY.MM.dd}" 
    }
  }
}

Elasticsearch

In order for Elasticsearch to correctly handle our DHCP data , we need to provide a index template. Notice that we are just using Keywords here.

{
  "dhcp": {
    "order": 10,
    "index_patterns": [
      "dhcp-*"
    ],
    "settings": {},
    "mappings": {
      "dhcp": {
        "dynamic_templates": [
          {
            "strings_as_keyword": {
              "mapping": {
                "ignore_above": 1024,
                "type": "keyword"
              },
              "match_mapping_type": "string"
            }
          }
        ],
        "properties": {}
      }
    },
    "aliases": {}
  }
}

That’s is pretty much it for you to get data going. We will leave  it for you to define a dashboard to display the data in a meaningful manner.

Stay tuned for next part of this series where we will be expanding the logstash filter by enriching the data with Mac Vendor lookup.