The Setup..

When adding a net new data source to Metron, the first step is to decide how to push the events from the new telemetry data source into Metron. You can use a number of data collection tools and that decision is decoupled from Metron. An excellent tool for pushing data into Metron is  Apache Nifi which this section will describe how to use. The second step is to configure Metron to parse the telemetry data source so that downstream processing can be done on it. In this article we will walk you through how to perform both of these steps.

In the section , we described the following set of requirements for Customer Foo who wanted to add the Squid telemetry data source Into Metron.

1. The proxy events from Squid logs need to be ingested in real-time.
2. The proxy logs must be parsed into a standardized JSON structure that Metron can understand.
3. In real-time, the squid proxy event must be enriched so that the domain names are enriched with the IP information.
4. In real-time, the IP within the proxy event must be checked for threat intel feeds.
5. If there is a threat intel hit, an alert needs to be raised.
6. The end user must be able to see the new telemetry events and the alerts from the new data source.
7. All of these requirements will need to be implemented easily without writing any new Java code.

In this article, we will walk you through how to perform steps 1, 2, and 6.

Install Metron First

You will need to install Metron first. Today, there are three options to install Metron: Metron Installation Options. Chose any of the options and below instructions should be applicable to all three install options given the following variables that you will need to plugin with your own values:

• KAFKA_HOST = host where a Kafka broker is installed
• ZOOKEEPER_HOST = host where a Zookeeper server is installed
• PROBE_HOST = Host where your sensor, probes are installed. If don't have any sensors installed, pick the host where a storm supervisor is running
• SQUID_HOST = Host where you want to install SQUID. If you don't care, just install on the PROBE_HOST
• NIFI_HOST = The host where you will install NIFI. You want this this to be same host that you installed Squid.
• HOST_WITH_ENRICHMENT_TAG = This is the host in your inventory hosts file that you put under the group "enrichment" 
• SEARCH_HOST = This is the host where you have elastic or solr running. This is the host in your inventory hosts file that you put under the group "search". Pick one of the search hosts
• SEARCH_HOST_PORT  = The port of the search host where indexing is configured. (e.g: 9300)
• METRON_UI_HOST = This is the host where your metron ui web application is running. This is the host in your inventory hosts file that you put under the group "web".
• METRON_VERSION = The release of the metron binaries you are working with (e.g: 0.2.0BETA-RC2)

Parsing the Squid Telemetry Data Source in Metron

The following steps guide you through how to add this new telemetry.

Step 1: Install the Squid Sensor

1. ssh into $SQUID_HOST
2. Install and start Squid:

   sudo yum install squid
   sudo service squid start

3. With Squid started, look at the the different log files that get created:

   sudo su -
   cd /var/log/squid
   ls

   You see that there are three types of logs available: access.log, cache.log, and squid.out. We are interested in access.log becasuse that is the log that records the proxy usage.

4. Initially the access.log is empty. Let's generate a few entries for the log, then list the new contents of the access.log:

   squidclient "http://www.aliexpress.com/af/shoes.html?ltype=wholesale&d=y&origin=n&isViewCP=y&catId=0&initiative_id=SB_20160622082445&SearchText=shoes"
   squidclient "http://www.help.1and1.co.uk/domains-c40986/transfer-domains-c79878"
   squidclient "http://www.pravda.ru/science/"
   squidclient "https://www.google.com/maps/place/Waterford,+WI/@42.7639877,-88.2867248,12z/data=!4m5!3m4!1s0x88059e67de9a3861:0x2d24f51aad34c80b!8m2!3d42.7630722!4d-88.2142563"
   squidclient "http://www.brightsideofthesun.com/2016/6/25/12027078/anatomy-of-a-deal-phoenix-suns-pick-bender-chriss"
   squidclient "https://www.microsoftstore.com/store/msusa/en_US/pdp/Microsoft-Band-2-Charging-Stand/productID.329506400"
   squidclient "http://www.autonews.com/article/20151115/RETAIL04/311169971/toyota-fj-cruiser-is-scarce-hot-and-high-priced"
   squidclient "https://tfl.gov.uk/plan-a-journey/"
   squidclient "https://www.facebook.com/Africa-Bike-Week-1550200608567001/"
   squidclient "http://www.ebay.com/itm/02-Infiniti-QX4-Rear-spoiler-Air-deflector-Nissan-Pathfinder-/172240020293?fits=Make%3AInfiniti%7CModel%3AQX4&hash=item281a4e2345:g:iMkAAOSwoBtW4Iwx&vxp=mtr"
   squidclient "http://www.recruit.jp/corporate/english/company/index.html"
   squidclient "http://www.lada.ru/en/cars/4x4/3dv/about.html"
   squidclient "http://www.help.1and1.co.uk/domains-c40986/transfer-domains-c79878"
   squidclient "http://www.aliexpress.com/af/shoes.html?ltype=wholesale&d=y&origin=n&isViewCP=y&catId=0&initiative_id=SB_20160622082445&SearchText=shoes"

   In production environments you would configure your users web browsers to point to the proxy server, but for the sake of simplicity of this tutorial we will use the client that is packaged with the Squid installation. After we use the client to simulate proxy requests, the Squid log entries should look as follows:

   1467011157.401 415 127.0.0.1 TCP_MISS/200 337891 GEThttp://www.aliexpress.com/af/shoes.html? - DIRECT/207.109.73.154 text/html
   1467011158.083 671 127.0.0.1 TCP_MISS/200 41846 GEThttp://www.help.1and1.co.uk/domains-c40986/transfer-domains-c79878 - DIRECT/212.227.34.3 text/html
   1467011159.978 1893 127.0.0.1 TCP_MISS/200 153925 GEThttp://www.pravda.ru/science/ - DIRECT/185.103.135.90 text/html

5. Using the Squid log entries, we can determine the format of the log entires which is:

   timestamp | time elapsed | remotehost | code/status | bytes | method | URL rfc931 peerstatus/peerhost | type

Step 2: Create a Kafka Topic for the New Data Source

Step 3: Create a Grok Statement to Parse the Squid Telemetry Event

Now we are ready to tackle the Metron parsing topology setup.

1. The first thing we need to do is decide if we will be using the Java-based parser or the Grok-based parser for the new telemetry. In this example we will be using the Grok parser. Grok parser is perfect for structured or semi-structured logs that are well understood (check) and telemetries with lower volumes of traffic (check).
2. Next we need to define the Grok expression for our log. Refer to Grok documentation for additional details. In our case the pattern is:

    

   SQUID_DELIMITED %{NUMBER:timestamp}%{SPACE:UNWANTED} %{INT:elapsed}%{SPACE:UNWANTED}%{IPV4:ip_src_addr} %{WORD:action}/%{NUMBER:code} %{NUMBER:bytes} %{WORD:method} %{NOTSPACE:url} - %{WORD:UNWANTED}\/%{IPV4:ip_dst_addr} %{WORD:UNWANTED}\/%{WORD:UNWANTED}

    

3. Notice that we apply the UNWANTED tag for any part of the message that we don't want included in our resulting JSON structure. Finally, notice that we applied the naming convention to the IPV4 field by referencing the following list of field conventions.

4. The last thing we need to do is to validate the Grok pattern to make sure it's valid. For our test we will be using a free Grok validator called Grok Constructor. A validated Grok expression should look like this:

   

5. 

6. Now that the Grok pattern has been defined, we need to save it and move it to HDFS. 
   1. ssh into HOST $HOST_WITH_ENRICHMENT_TAG as root
   2. Create a file called "squid" in the tmp directory and copy the Grok pattern into the file.
      1. touch /tmp/squid
      2. Open up the squid file add the grok pattern defined above
   3. put the squid file into the directory where Metron stores its Grok parsers. Existing Grok parsers that ship with Metron are staged under /apps/metron/pattern
      1. su - hdfs
      2. hadoop fs -rmr /apps/metron/patterns/squid
      3. hdfs dfs -put /tmp/squid /apps/metron/patterns/

Step 4: Parse and Transform the Squid Message

Now that the Grok pattern is staged in HDFS we need to define a parser configuration for the Metron Parsing Topology.  The configurations are kept in Zookeeper so the sensor configuration must be uploaded there after it has been created.

1. ssh into Host $HOST_WITH_ENRICHMENT_TAG as root
2. Create a Squid Grok parser configuration file at /usr/metron/$METRON_VERSION/config/zookeeper/parsers/squid.json with the following contents: 

   
   {
    "parserClassName": "org.apache.metron.parsers.GrokParser",
    "sensorTopic": "squid",
    "parserConfig": {
    "grokPath": "/apps/metron/patterns/squid",
    "patternLabel": "SQUID_DELIMITED",
    "timestampField": "timestamp"
    },
    "fieldTransformations" : [
      {
        "transformation" : "MTL"
       ,"output" : [ "full_hostname", "domain_without_subdomains" ]
       ,"config" : {
                     "full_hostname" : "URL_TO_HOST(url)"
                    ,"domain_without_subdomains" : "DOMAIN_REMOVE_SUBDOMAINS(full_hostname)"
                   }
       }
     ]

    }

    

3. Notice the use of the fieldTransformations in the parser configuration.  Our Grok Parser is set up to extract the URL, but really we want just the domain or even the domain without subdomains.  To do this, we can use the Metron Transformation Language field transformation.  The Metron Transformation Language is a Domain Specific Language which allows users to define extra transformations to be done on the messages flowing through the topology.  It supports a wide range of common network and string related functions as well as function composition and list operations.  In our case, we extract the hostname from the URL via the URL_TO_HOST function and remove the domain names with DOMAIN_REMOVE_SUBDOMAINS thereby creating two new fields, "full_hostname" and "domain_without_subdomains" to each message. 
4. All parser configurations are stored in Zookeeper. A script is provided to upload configurations to Zookeeper. 
   1. /usr/metron/$METRON_VERSION/bin/zk_load_configs.sh --mode PUSH -i /usr/metron/$METRON_VERSION/config/zookeeper -z $ZOOKEEPER_HOST:2181 

Step 5: Validate the Squid Message

The below describes the validation configuration you see above.

More details on the validation framework can be found in the Validation Framework section here: https://github.com/apache/incubator-metron/tree/master/metron-platform/metron-common#transformation-language

Step 6: Deploy the new Parser Topology

1. Log into HOST $HOST_WITH_ENRICHMENT_TAG as root
2. Deploy the new squid paser topology:

   /usr/metron/$METRON_VERSION/bin/start_parser_topology.sh -k $KAFKA_HOST:6667 -z $ZOOKEEPER_HOST:2181 -s squid

3. Go to the Storm UI and you should now see new "squid" topology and ensure that the topology has no errors

Using Apache Nifi to Stream data into Metron

Put simply NiFi was built to automate the flow of data between systems. Hence it is a fantastic tool to collect, ingest and push data to Metron. The below instructions on how to install configure and create the nifi flow to push squid events into Metron.

Install, Configure and and Start Apache Nifi

The following shows how to install Nifi on the VM. Do the following as root:

1. ssh into HOST $NIFI_HOST
2. Download Nifi

   cd /usr/lib
   wget  http://public-repo-1.hortonworks.com/HDF/centos6/1.x/updates/1.2.0.0/HDF-1.2.0.0-91.tar.gz
   tar -zxvf HDF-1.2.0.0-91.tar.gz 

3. Edit Nifi Configuration to update the port of the nifi web app: nifi.web.http.port=8089

   cd HDF-1.2.0.0/nifi
   vi  conf/nifi.properties
   //update nifi.web.http.port to 8089

4. Install Nifi as service

   bin/nifi.sh install nifi

5. Start the Nifi Service

   service nifi start

6. Go to the Nifi Web: http://$NIFI_HOST:8089/nifi/

Create a Nifi Flow to stream events to Metron

Now we will create a flow to capture events from squid and push them into metron

1. Drag a processor to the canvas (do this by the dragging the processor icon..first icon)
2. Search for TailFile processor and select Add. Right click on the processor and configure. In settings tab change the name to "Ingest Squid Events"
   1. In properties, configure the following like the following:
   1. 
3. Drag Another Processor the canvas
4. Search for PutKafka and select Add
5. Right click on the processor and configure. In Settings, change names to "Stream to Metron” click the checkbox for failure and success for relationship.
6. Under properties, set 3 properties
   1. Known Brokers: $KAFKA_HOST:6667
   2. Topic Name: squid
   3. Client Name: nifi-squid
7. Create a connection by dragging the arrow from Ingest Squid Events to Stream to Metron
8. Select the entire Flow and click the play button (play button). you should see all processors green like the below:
   1. 
9. Generate some data using squidclient (do this for about 20+ sites)

   squidclient http://www.cnn.com

10. You should see metrics on the processor of data being pushed into Metron.
11. Look at the Storm UI for the parser topology and you should see tuples coming in
12. After about 5 minutes, you should see a new Elastic Search index called squid_index* in the Elastic Admin UI

Verify Events are Indexed

By convention the index where the new messages will be indexed is called squid_index_[timestamp] and the document type is squid_doc.

In order to verify that the messages were indexed correctly, we can use the elastic search Head plugin.

1. ssh into Host $SEARCH_HOST
2. Install the head plugin

   usr/share/elasticsearch/bin/plugin -install mobz/elasticsearch-head/1.x 

3. Navigate to elastic head UI: http://SEARCH_HOST:9200/_plugin/head/
4. Click on Browser tab and select squid doc on the left panel and then select one of th sample docs. You should see something like the following:
   1. 

Configure Metron UI to view the Squid Telemetry Events

Now that we have Metron configured to parse, index and persist telemetry events and Nifi pushing data to Metron, lets now visualize this streaming telemetry data in the Metron UI. We will be adding 3 new panels to visualize the Squid Events: Histogram Panel, Count Panel and Detail Panel

Adding Squid Event Count Panel to Dashboard

1. Log into the Metron UI Dashboard: http://METRON_UI_HOST:5000
2. Select "Visualize" Tab --> Select "Metric" Visualization"= --> Select "From a new search" for Search Source --> Select "squid*" index source –> Click the Save disk icon on the top right
3. Name the Visualization "Squid Event Count" and click Save
4. Select "Dashboard" Tab --> Click the plus icon --> Select "Visualization" tab --> Search for "Squid Event Count" --> Select it
5. The visualization will be added to the bottom of the dashboard
6. Click the save icon on the top right to save the dashboard.
   
   

Creating a Histogram Panel

1. Log into the Metron UI Dashboard: http://METRON_UI_HOST:5000
2. Select "Visualize" Tab --> Select "Line Chart" Visualization --> Select "From a new search" for Search Source --> Select "squid*" index source 
3. Configure the Visualization like the following: 
   1. 
      
4. Click the Save Icon on the right right corner --> Name the Visualization "Squid Events Histogram" and click Save
5. Select "Dashboard" Tab --> Click the plus icon --> Select "Visualization" tab --> Search for "Squid Events Histogram" --> Select it
6. The visualization will be added to the bottom of the dashboard
7. Click the save icon on the top right to save the dashboard.

Adding a Detail Panel

1. Log into the Metron UI Dashboard: http://METRON_UI_HOST:5000
2. Select "Discover" Tab --> Select the "squid*" index
3. Search for only docs in this index with type of squid_doc
   1. Type the following in search "_type:  squid_doc" 
   2. click the search icon
4. Now we only to select subset of the fields that we want to display in the detail panel. In the left hand panel under "Available Fields", "add" the following fields:
   1. full_hostname
   2. ip_src_addr
   3. ip_dst_addr
   4. original_string
   5. method
   6. type
5. The discover/search panel should look something like the following:
   1. 
6. Click the "Save" icon on the top right corner  --> name the search "Squid Event Details" --> Click Save
7. Select "Dashboard" Tab --> Click the plus icon --> Select "Searches" tab --> Search for "Squid Event Details" --> Select it
8. The visualization will be added to the bottom of the dashboard
9. Click the save icon on the top right to save the dashboard.

The Dashboard with the 3 Squid Panels

The following is what the new dashboard would look like with the 3 squid panels added.