Substation

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<p align="center"><b>Substation is a toolkit for routing, normalizing, and enriching security event and audit logs.</b></p> <div align="center">

[Releases][releases] | [Documentation][docs] | [Adopters][adopters] | [Announcement (2022)][announcement] | [v1.0 Release (2024)][v1_release]

</div>

Quickstart

Want to see a demo before diving into the documentation? Run this command:

export PATH=$PATH:$(go env GOPATH)/bin && \
go install github.com/brexhq/substation/v2/cmd/substation@latest && \
substation demo

At a Glance

Substation is inspired by data pipeline systems like Logstash and Fluentd, but is built for modern security teams:

Extensible Data Processing: Build data processing pipeline systems and microservices using out-of-the-box applications and 100+ data transformation functions, or create your own written in Go.
Route Data Across the Cloud: Conditionally route data to, from, and between AWS cloud services, including S3, Kinesis, SQS, and Lambda, or to any HTTP endpoint.
Bring Your Own Schema: Format, normalize, and enrich event logs to comply with the Elastic Common Schema (ECS), Open Cybersecurity Schema Framework (OCSF), or any other schema.
Unlimited Data Enrichment: Use external APIs to enrich event logs affordably and at scale with enterprise and threat intelligence, or build a microservice that reduces spend in expensive security APIs.
No Servers, No Maintenance: Deploys as a serverless application in your AWS account, launches in minutes using Terraform, and requires no maintenance after deployment.
Runs Almost Anywhere: Create applications that run on most platforms supported by Go and transform data consistently across laptops, servers, containers, and serverless functions.
High Performance, Low Cost: Transform 100,000+ events per second while keeping cloud costs as low as a few cents per GB. Vendor solutions, like Cribl and Datadog, can cost up to 10x more.

All of these data pipeline and microservice systems, and many more, can be built with Substation:

Example Substation architectures

Transforming Event Logs

Substation excels at formatting, normalizing, and enriching event logs. For example, Zeek connection logs can be transformed to comply with the Elastic Common Schema:

<table> <tr> <th><code>Original Event</code></th> <th><code>Transformed Event</code></th> </tr> <tr> <td>

{
  "ts": 1591367999.430166,
  "uid": "C5bLoe2Mvxqhawzqqd",
  "id.orig_h": "192.168.4.76",
  "id.orig_p": 46378,
  "id.resp_h": "31.3.245.133",
  "id.resp_p": 80,
  "proto": "tcp",
  "service": "http",
  "duration": 0.25411510467529297,
  "orig_bytes": 77,
  "resp_bytes": 295,
  "conn_state": "SF",
  "missed_bytes": 0,
  "history": "ShADadFf",
  "orig_pkts": 6,
  "orig_ip_bytes": 397,
  "resp_pkts": 4,
  "resp_ip_bytes": 511
}

</td> <td>

{
  "event": {
    "original": {
      "ts": 1591367999.430166,
      "uid": "C5bLoe2Mvxqhawzqqd",
      "id.orig_h": "192.168.4.76",
      "id.orig_p": 46378,
      "id.resp_h": "31.3.245.133",
      "id.resp_p": 80,
      "proto": "tcp",
      "service": "http",
      "duration": 0.25411510467529297,
      "orig_bytes": 77,
      "resp_bytes": 295,
      "conn_state": "SF",
      "missed_bytes": 0,
      "history": "ShADadFf",
      "orig_pkts": 6,
      "orig_ip_bytes": 397,
      "resp_pkts": 4,
      "resp_ip_bytes": 511
    },
    "hash": "af70ea0b38e1fb529e230d3eca6badd54cd6a080d7fcb909cac4ee0191bb788f",
    "created": "2022-12-30T17:20:41.027505Z",
    "id": "C5bLoe2Mvxqhawzqqd",
    "kind": "event",
    "category": [
      "network"
    ],
    "action": "network-connection",
    "outcome": "success",
    "duration": 254115104.675293
  },
  "@timestamp": "2020-06-05T14:39:59.430166Z",
  "client": {
    "address": "192.168.4.76",
    "ip": "192.168.4.76",
    "port": 46378,
    "packets": 6,
    "bytes": 77
  },
  "server": {
    "address": "31.3.245.133",
    "ip": "31.3.245.133",
    "port": 80,
    "packets": 4,
    "bytes": 295,
    "domain": "h31-3-245-133.host.redstation.co.uk",
    "top_level_domain": "co.uk",
    "subdomain": "h31-3-245-133.host",
    "registered_domain": "redstation.co.uk",
    "as": {
      "number": 20860,
      "organization": {
        "name": "Iomart Cloud Services Limited"
      }
    },
    "geo": {
      "continent_name": "Europe",
      "country_name": "United Kingdom",
      "city_name": "Manchester",
      "location": {
        "latitude": 53.5039,
        "longitude": -2.1959
      },
      "accuracy": 1000
    }
  },
  "network": {
    "protocol": "tcp",
    "bytes": 372,
    "packets": 10,
    "direction": "outbound"
  }
}

</td> </tr> </table>

Routing Data

Substation can route data to several destinations from a single process and, unlike most other data pipeline systems, data transformation and routing are functionally equivalent -- this means that data can be transformed or routed in any order.

In this configuration, data is:

Written to AWS S3
Printed to stdout
Conditionally dropped (filtered, removed)
Sent to an HTTPS endpoint

// The input is a JSON array of objects, such as:
// [
//   { "field1": "a", "field2": 1, "field3": true },
//   { "field1": "b", "field2": 2, "field3": false },
//   ...
// ]
local sub = import 'substation.libsonnet';

// This filters events based on the value of field3.
local is_false = sub.cnd.str.eq({ object: { source_key: 'field3' }, value: 'false' });

{
  transforms: [
    // Pre-transformed data is written to an object in AWS S3 for long-term storage.
    sub.tf.send.aws.s3({ aws: { arn: 'arn:aws:s3:::example-bucket-name' } }),
    // The JSON array is split into individual events that go through
    // the remaining transforms. Each event is printed to stdout.
    sub.tf.agg.from.array(),
    sub.tf.send.stdout(),
    // Events where field3 is false are removed from the pipeline.
    sub.pattern.tf.conditional(condition=is_false, transform=sub.tf.util.drop()),
    // The remaining events are sent to an HTTPS endpoint.
    sub.tf.send.http.post({ url: 'https://example-http-endpoint.com' }),
  ],
}

Alternatively, the data can be conditionally routed to different destinations:

local sub = import 'substation.libsonnet';

{
  transforms: [
    // If field3 is false, then the event is sent to an HTTPS endpoint; otherwise,
    // the event is written to an object in AWS S3.
    sub.tf.meta.switch({ cases: [
      {
        condition: sub.cnd.str.eq({ object: { source_key: 'field3' }, value: 'false' }),
        transforms: [
          sub.tf.send.http.post({ url: 'https://example-http-endpoint.com' }),
        ],
      },
      {
        transforms: [
          sub.tf.send.aws.s3({ aws: { arn: 'arn:aws:s3:::example-bucket-name' } }),
        ],
      },
    ] }),
    // The event is always available to any remaining transforms.
    sub.tf.send.stdout(),
  ],
}

Configuring Applications

Substation applications run almost anywhere (laptops, servers, containers, serverless functions) and all transform functions behave identically regardless of where they are run. This makes it easy to develop configuration changes locally, validate them in a build (CI/CD) pipeline, and run integration tests in a staging environment before deploying to production.

Configurations are written in Jsonnet and can be expressed as functional code, simplifying version control and making it easy to build custom data processing libraries. For power users, configurations also have abbreviations that make them easier to write. Compare the configuration below to similar configurations for Logstash and Fluentd:

<table> <tr> <th><code>Substation</code></th> <th><code>Logstash</code></th> <th><code>Fluentd</code></th> </tr> <tr> <td>

local sub = import 'substation.libsonnet';

{
  transforms: [
    sub.tf.obj.cp({ object: { source_key: 'src_field_1', target_key: 'dest_field_1' } }),
    sub.tf.obj.cp({ obj: { src: 'src_field_2', trg: 'dest_field_2' } }),
    sub.tf.send.stdout(),
    sub.tf.send.http.post({ url: 'https://example-http-endpoint.com' }),
  ],
}

</td> <td>

input {
  file {
    path => "/path/to/your/file.log"
    start_position => "beginning"
    sincedb_path => "/dev/null"
    codec => "json"
  }
}

filter {
  json {
    source => "message"
  }

  mutate {
    copy => { "src_field_1" => "dest_field_1" }
    copy => { "src_field_2" => "dest_field_2" }
  }
}

output {
  stdout {
    codec => rubydebug
  }

  http {
    url => "https://example-http-endpoint.com"
    http_method => "post"
    format => "json"
  }
}

</td> <td>

<source>
  @type tail
  path /path/to/your/file.log
  pos_file /dev/null
  tag file.log
  format json
</source>

<filter file.log>
  @type record_transformer
  enable_ru

Substation

Install / Use

README

Substation

Quickstart

At a Glance

Transforming Event Logs

Routing Data

Configuring Applications