Logprep allows to collect, process and forward log messages from various data sources. Log messages are being read and written by so-called connectors. Currently, connectors for Kafka, Elasticsearch, Opensearch and JSON(L) files exist. Additionally, an Input Connector for HTTP Input is provided, which starts an uvicorn server and accepts log message via POST Requests.
The log messages are processed step-by-step by a pipeline of processors, where each processor modifies an event that is being passed through. The main idea is that each processor performs a simple task that is easy to carry out. Once the log massage is passed through all processors in the pipeline the resulting message is sent to a configured output connector.
Logprep is designed to be expandable with new connectors and processors.
Logprep is primarily designed to process log messages. Generally, Logprep can handle JSON messages, allowing further applications besides log handling.
This readme provides basic information about the following topics:
More detailed information can be found in the Documentation.
Logprep processes incoming log messages with a configured pipeline that can be spawned
multiple times via multiprocessing.
The following chart shows a basic setup that represents this behaviour.
The pipeline consists of three processors: the Dissector, Geo-IP Enricher and the
Dropper.
Each pipeline runs concurrently and takes one event from it's Input Connector.
Once the log messages is fully processed the result will be forwarded to the Output Connector,
after which the pipeline will take the next message, repeating the processing cycle.
flowchart LR
A1[Input\nConnector] --> B
A2[Input\nConnector] --> C
A3[Input\nConnector] --> D
subgraph Pipeline 1
B[Dissector] --> E[Geo-IP Enricher]
E --> F[Dropper]
end
subgraph Pipeline 2
C[Dissector] --> G[Geo-IP Enricher]
G --> H[Dropper]
end
subgraph Pipeline n
D[Dissector] --> I[Geo-IP Enricher]
I --> J[Dropper]
end
F --> K1[Output\nConnector]
H --> K2[Output\nConnector]
J --> K3[Output\nConnector]
Every processor has one simple task to fulfill.
For example, the Dissector can split up long message fields into multiple subfields
to facilitate structural normalization.
The Geo-IP Enricher, for example, takes an ip-address and adds the geolocation of it to the
log message, based on a configured geo-ip database.
Or the Dropper deletes fields from the log message.
As detailed overview of all processors can be found in the processor documentation.
To influence the behaviour of those processors, each can be configured with a set of rules. These rules define two things. Firstly, they specify when the processor should process a log message and secondly they specify how to process the message. For example which fields should be deleted or to which IP-address the geolocation should be retrieved.
For performance reasons on startup all rules per processor are aggregated to a generic and a specific rule tree, respectively. Instead of evaluating all rules independently for each log message the message is checked against the rule tree. Each node in the rule tree represents a condition that has to be meet, while the leafs represent changes that the processor should apply. If no condition is met, the processor will just pass the log event to the next processor.
The following chart gives an example of such a rule tree:
flowchart TD
A[root]
A-->B[Condition 1]
A-->C[Condition 2]
A-->D[Condition 3]
B-->E[Condition 4]
B-->H(Rule 1)
C-->I(Rule 2)
D-->J(rule 3)
E-->G(Rule 4)
To further improve the performance, it is possible to prioritize specific nodes of the rule tree,
such that broader conditions are higher up in the tree.
And specific conditions can be moved further down.
Following json gives an example of such a rule tree configuration.
This configuration will lead to the prioritization of tags and message in the rule tree.
{
"priority_dict": {
"category": "01",
"message": "02"
},
"tag_map": {
"check_field_name": "check-tag"
}
}Instead of writing very specific rules that apply to single log messages, it is also possible to define generic rules that apply to multiple messages. It is possible to define a set of generic and specific rules for each processor, resulting in two rule trees.
Connectors are responsible for reading the input and writing the result to a desired output. The main connectors that are currently used and implemented are a kafka-input-connector and a kafka-output-connector allowing to receive messages from a kafka-topic and write messages into a kafka-topic. Addionally, you can use the Opensearch or Elasticsearch output connectors to ship the messages directly to Opensearch or Elasticsearch after processing.
The details regarding the connectors can be found in the input connector documentation and output connector documentation.
To run Logprep, certain configurations have to be provided. Because Logprep is designed to run in a containerized environment like Kubernetes, these configurations can be provided via the filesystem or http. By providing the configuration via http, it is possible to control the configuration change via a flexible http api. This enables Logprep to quickly adapt to changes in your environment.
First, a general configuration is given that describes the pipeline and the connectors, and lastly, the processors need rules in order to process messages correctly.
The following yaml configuration shows an example configuration for the pipeline shown in the graph above:
process_count: 3
timeout: 0.1
pipeline:
- dissector:
type: dissector
specific_rules:
- https://your-api/dissector/
generic_rules:
- rules/01_dissector/generic/
- geoip_enricher:
type: geoip_enricher
specific_rules:
- https://your-api/geoip/
generic_rules:
- rules/02_geoip_enricher/generic/
tree_config: artifacts/tree_config.json
db_path: artifacts/GeoDB.mmdb
- dropper:
type: dropper
specific_rules:
- rules/03_dropper/specific/
generic_rules:
- rules/03_dropper/generic/
input:
mykafka:
type: confluentkafka_input
bootstrapservers: [127.0.0.1:9092]
topic: consumer
group: cgroup
auto_commit: true
session_timeout: 6000
offset_reset_policy: smallest
output:
opensearch:
type: opensearch_output
hosts:
- 127.0.0.1:9200
default_index: default_index
error_index: error_index
message_backlog_size: 10000
timeout: 10000
max_retries:
user: the username
secret: the passord
cert: /path/to/cert.crtThe following yaml represents a dropper rule which according to the previous configuration
should be in the rules/03_dropper/generic/ directory.
filter: "message"
drop:
- message
description: "Drops the message field"The condition of this rule would check if the field message exists in the log.
If it does exist then the dropper would delete this field from the log message.
Details about the rule language and how to write rules for the processors can be found in the rule language documentation.
Python should be present on the system, currently supported are the versions 3.9 - 3.11.
You have to install hyperscan and rdkafka before installing logprep. On debian based
systems, this is performed by sudo apt-get update && sudo apt-get -y install libhyperscan-dev librdkafka-dev pkg-config.
To install Logprep you have following options:
1. Option: Installation via PyPI:
This option is recommended if you just want to use the latest release of logprep.
pip install logprep
2. Option: Installation via Git Repository:
This option is recommended if you are interested in the latest developments and might want to contribute to them.
git clone https://github.com/fkie-cad/Logprep.git
cd Logprep
pip install -r requirements.txt
3. Option: Installation via Github Release
This option is recommended if you just want to try out the latest developments.
pip install git+https://github.com/fkie-cad/Logprep.git@latest
4. Option: Docker build from current commit
This option can be used to build a container image from a specific commit
docker build -t logprep .
docker run logprep --help
Tox can be used to perform unit and acceptance tests (install tox via pip3 install tox).
Tests are started by executing tox in the project root directory.
This creates a virtual test environment and executes tests within it.
Multiple different test environments were defined for tox.
Those can be executed via: tox -e [name of the test environment].
For Example:
tox -e py39-all
This runs all tests, calculates the test coverage and evaluates the code quality for the python 3.9 version.
Multiple environments can be tested within one call:
tox -e py39-all -e py310-all -e py311-all
If you want to run them in parallel attach the option -p.
This can lead to side effects in I/O operations though, like concurrences in writing or reading
files.
An overview of the test environments can be obtained by executing:
tox -av
In case the requirements change, the test environments must be rebuilt with the parameter -r:
tox -e py39 -e py310 -e py311 -r
If you want to run semgrep rules run
pip install semgrep
Afterwards you can just call the tox environment with for example
tox -e py39-semgrep
Depending on how you have installed Logprep you have different choices to run Logprep as well.
If you have installed it via PyPI or the Github Development release just run:
logprep $CONFIG
If you have installed Logprep via cloning the repository then you should run it via:
PYTHONPATH="." python3 logprep/run_logprep.py $CONFIG
Where $CONFIG is the path or uri to a configuration file (see the documentation about the
configuration).
The next sections all assume an installation via pip
The following command can be executed to verify the configuration file without having to run Logprep:
logprep --verify-config $CONFIG
Where $CONFIG is the path or uri to a configuration file (see the documentation about the
configuration).
The following command can be executed to validate the schema and the rules:
logprep --validate-rules $CONFIG
Where $CONFIG is the path or uri to a configuration file (see the documentation about the
configuration).
Alternatively, the validation can be performed directly. Assuming you have cloned the repository from git.
PYTHONPATH="." python3 logprep/util/schema_and_rule_checker.py --labeling-schema $LABELING_SCHEMA --labeling-rules $LABELING_RULES
Where $LABELING_SCHEMA is the path to a labeling-schema (JSON file) and $LABELING_RULES is
the path to a directory with rule files (JSON/YML files, see Rules.md, subdirectories
are permitted)
Analogously, --normalization-rules and --pseudonymizer-rules can be used.
Validation does also perform a verification of the pipeline section of the Logprep configuration.
To change the configuration of Logprep it is not needed to restart Logprep entirely.
Instead, it can be issued to reload the configuration.
For this, the signal SIGUSR1 must be send to the Logprep process.
Additionally, a config_refresh_interval can be set to periodically and automatically refresh the given configuration.
This can be useful in case of containerized environments (such as Kubernetes), when pod volumes often change
on the fly.
If the configuration does not pass a consistency check, then an error message is logged and Logprep keeps running with the previous configuration. The configuration should be then checked and corrected on the basis of the error message.
Logprep was designed to work with the Elastic Stack or Opensearch and Kafka. This repository comes with a docker-compose file that builds a pre-configured Elastic Stack with Kafka and Logprep. To get it running docker and docker-compose (version >= 1.28) must be first installed. The docker-compose file is located in the directory quickstart.
Before running, docker-compose sysctl -w vm.max_map_count=262144 must be executed.
Otherwise, Elasticsearch is not properly started.
The environment can either be started with a Logprep container or without one.
To start it without Logprep, the command docker-compose up -d can be executed from within
the quickstart directory.
It starts and connects Kafka, ZooKeeper, Logstash, Elasticsearch and Kibana.
Logprep can be then started from the host once the quickstart environment is running and
fully loaded.
The Logprep configuration file quickstart/exampledata/config/pipeline.yml can be used to
connect to the quickstart environment.
To start the whole environment including a Logprep container, the
command docker-compose --profile logprep up -d can be executed.
It is now possible to write JSON events into Kafka and read the processed events in Kibana.
Once everything has started, Kibana can be accessed by a web-browser with the
address 127.0.0.1:5601.
Kafka can be accessed with the console producer and consumer from Kafka with the
address 127.0.0.1:9092 or from within the docker container Kafka.
The table below shows which ports have been exposed on localhost for the services.
| Kafka | ZooKeeper | Elasticsearch | Kibana | |
|---|---|---|---|---|
| Port | 9092 | 2181 | 9200 | 5601 |
The example rules that are used in the docker instance of Logprep can be found
in quickstart/exampledata/rules.
Example events that trigger for the example rules can be found in
quickstart/exampledata/input_logdata/test_input.jsonl.
These events can be added to Kafka with the Kafka console producer within the Kafka container by
executing the following command:
(docker exec -i kafka /opt/kafka/bin/kafka-console-producer.sh --bootstrap-server 127.0.0.1:9092 --topic consumer) < exampledata/input_logdata/test_input.jsonl
Once the events have been processed for the first time, the new indices processed, sre and pseudonyms should be available in Kibana.
The environment can be stopped via docker-compose down.
The documentation for Logprep is online at https://logprep.readthedocs.io/en/latest/ or it can
be built locally via tox (install via pip3 install tox).
Building the documentation is done by executing the following command from within
the project root directory:
tox -e py39-docs
A HTML documentation can be then found in doc/_build/html/index.html.
Every contribution is highly appreciated. If you have ideas or improvements feel free to create a fork and open a pull requests. Issues and engagement in open discussions are also welcome.
Logprep is distributed under the LGPL-2.1 License. See LICENSE file for more information.