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Merge pull request #2522 from sebix/docs
docs: fix whitespaces in various places
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docs/admin/common-problems.md

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-->
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# Common Problems
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# Common Problems
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## IntelMQ
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docs/admin/configuration/intelmq.md

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- `/var/log/intelmq/` (logs)
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- `/var/lib/intelmq/` (local states)
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- `/var/run/intelmq/` (PID files)
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Otherwise, the configuration directory is `/opt/intelmq/etc/`. Using the environment variable `INTELMQ_ROOT_DIR` allows setting any arbitrary root directory.
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You can switch this by setting the environment variables `INTELMQ_PATHS_NO_OPT` and `INTELMQ_PATHS_OPT`, respectively.
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This is the main configuration file. It uses YAML format since IntelMQ 3.0. It consists of two parts:
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* Global Configuration
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* Individual Bot Configuration
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* Individual Bot Configuration
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!!! warning
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Comments in YAML are currently not preserved by IntelMQ (known bug [#2003](https://github.com/certtools/intelmq/issues/2003)).
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**`error_dump_message`**
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(required, boolean) Specifies if the bot will write queued up messages to its dump file (use intelmqdump to
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re-insert the message).
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re-insert the message).
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If the path `_on_error` exists for a bot, the message is also sent to this queue, instead of (only) dumping the file if
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configured to do so.
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(optional, string) Allowed values are `redis` and `amqp`. Selects the message broker IntelMQ should use. As this parameter can be overridden by each bot, this allows usage of different broker systems and hosts, as well as switching between them on the same IntelMQ instance. Defaults to `redis`.
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- **redis** - Please note that persistence has to be [manually activated](http://redis.io/topics/persistence).
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- **redis** - Please note that persistence has to be [manually activated](http://redis.io/topics/persistence).
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- **amqp** - [Using the AMQP broker]() is currently beta but there are no known issues. A popular AMQP broker is [RabbitMQ](https://www.rabbitmq.com/).
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**`destination_pipeline_broker`**
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**`destination_queues`**
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(optional, object) Bots can have multiple destination queues. Destination queues can also be grouped into **named paths**. There are two special path names `_default` and `_on_error`. The path `_default` is used if the path is not is specified by the bot itself (which is the most common case). In case of an error during the processing, the message will be sent to the `_on_error` path if specified (optional).
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(optional, object) Bots can have multiple destination queues. Destination queues can also be grouped into **named paths**. There are two special path names `_default` and `_on_error`. The path `_default` is used if the path is not is specified by the bot itself (which is the most common case). In case of an error during the processing, the message will be sent to the `_on_error` path if specified (optional).
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Only few of the bots (mostly expert bots with filtering capabilities) can take advantage of arbitrarily named paths. Some expert bots are capable of sending messages to paths, this feature is explained in their documentation, e.g. the [Filter](../../user/bots.md#intelmq.bots.experts.filter.expert) expert and the [Sieve](../../user/bots.md#intelmq.bots.experts.sieve.expert) expert.
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docs/admin/database/elasticsearch.md

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redis_queue: logstash-queue
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```
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!!! warning
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!!! warning
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You will not be able to monitor this redis queue via IntelMQ Manager.
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### Configuring Logstash
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redis {
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host => "10.10.10.10"
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port => 6379
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db => 4
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db => 4
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data_type => "list"
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key => "logstash-queue"
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}

docs/admin/database/postgresql.md

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### How to setup
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Thanks to TimescaleDB its very easy to setup.
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Thanks to TimescaleDB its very easy to setup.
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1. Choose your preferred
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[Timescale

docs/admin/installation/pypi.md

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This guide provides instruction on how to install IntelMQ and it's components using the Python Package Index (PyPI)
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repository.
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!!! note
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!!! note
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Some bots may have additional dependencies which are mentioned in their own documentation.
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## Installing IntelMQ

docs/admin/management/intelmq.md

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enabled bot is not running, the exit code is 1. The same is valid for
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e.g. `intelmqctl status`, which can be used for monitoring,
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and all other operations.
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## Error Handling
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When bots are failing due to bad input data or programming errors, they can dump the problematic message to a file along

docs/dev/library.md

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```
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As the bot is not fully initialized, we can process messages now.
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Inserting a message as dictionary:
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Inserting a message as dictionary:
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```python
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queues = domain_suffix.process_message({'source.fqdn': 'www.example.com'})

docs/unsorted/intelmq-3.0-architecture.md

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_Task_: create a setup where each bot MAY run in a docker container
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_Background_: It might make sense to be able to run each bot in a docker container since it fits with a lot of new paradigms in orchestration.
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With a proper template, each bot running in a docker container could send its logs to some central logger (for example splunk or similar) and
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With a proper template, each bot running in a docker container could send its logs to some central logger (for example splunk or similar) and
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the sysadmin/devops teams which are already using these systems for monitoring alerts can properly fit the IntelMQ logs and alerts to their regular daily routine.
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Docker also allows the sysadmin/devops folks to centrally manage the system.
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_Task_: create tutorials with VMs/docker images.
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_Background_:
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We are missing good tutorials ("playbooks") on how to run certain workflows via IntelMQ. Ideally, we would offer ready-made VMs/docker images where people who want to
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We are missing good tutorials ("playbooks") on how to run certain workflows via IntelMQ. Ideally, we would offer ready-made VMs/docker images where people who want to
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try out IntelMQ (and consequently adapt the setup to their own needs). This also helps teachers/presenters who want to demo IntelMQ.
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Specifically we would like to have:

docs/user/api.md

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```json
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{"login_token":"68b329da9893e34099c7d8ad5cb9c940","username":"$username"}
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```
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2. Using the login token to fetch data:
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```bash
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curl --location "http://localhost/intelmq/v1/api/version" \

docs/user/intro.md

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- Bots are divided into following groups:
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- **Collectors** - bots that collect data from sources such as website, mailbox, api, etc.
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- **Parsers** - bots that split and parse collected data into individual events.
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- **Experts** - bots that can do additional processing of events such as enriching, filtering, etc.

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