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Map Data to OCSF

In this tutorial you'll learn how to map events to Open Cybersecurity Schema Framework (OCSF). We walk you through an example of events from a network monitor and show how you can use Tenzir pipelines to easily transform them so that they become OCSF-compliant events.

The diagram above illustrates the data lifecycle and the OCSF mapping takes place: you collect data from various data sources, each of which has a different shape, and then convert them to a standardized representation. The primary benefit is that normalization decouples data acquisition from downstream analytics, allowing the processes to scale independently.

OCSF Primer​

The OCSF is a vendor-agnostic event schema (aka. "taxonomy") that defines structure and semantics for security events. Here are some key terms you need to know to map events:

  • Attribute: a unique identifier for a specific type, e.g., parent_folder of type String or observables of type Observable Array.
  • Event Class: the description of an event defined in terms of attributes, e.g., HTTP Activity and Detection Finding.
  • Category: a group of event classes, e.g., System Activity or Findings.

The diagram below illustrates how subsets of attributes form an event class:

The Base Event Class is a special event class that's part of every event class. Think of it as a mixin of attributes that get automatically added:

For this tutorial, we look at OCSF from the perspective of the mapper persona, i.e., as someone who converts existing events into the OCSF schema. OCSF also defines three other personas, author, producer, and analyst. These are out of scope. Our mission as mapper is now to study the event semantics of the data source we want to map, and translate the event to the appropriate OCSF event class.

Case Study: Zeek Logs​

Let's map some Zeek logs to OCSF!

What is Zeek?

The Zeek network monitor turns raw network traffic into detailed, structured logs. The logs range across the OSI stack from link layer activity to application-specific messages. In addition, Zeek provides a powerful scripting language to act on network events, making it a versatile tool for writing network-based detections to raise alerts.

Zeek generates logs in tab-separated values (TSV) or JSON format. Here's an example of a connection log in TSV format:

conn.log (TSV)
#separator \x09
#set_separator  ,
#empty_field    (empty)
#unset_field    -
#path   conn
#open   2014-05-23-18-02-04
#fields ts  uid id.orig_h   id.orig_p   id.resp_h   id.resp_p   proto   service duration    orig_bytes  resp_bytes  conn_state  local_orig  missed_bytes    history orig_pkts   orig_ip_bytes   resp_pkts   resp_ip_bytes   tunnel_parents
#types  time    string  addr    port    addr    port    enum    string  interval    count   count   string  bool    count   string  count   count   count   count   table[string]
1637155966.565338   C5luJD1ATrGDOcouW2  89.248.165.145  43831   198.71.247.91   52806   -   -   tcp -   -   -   -   S0  -   -   0   S   1   40  0   0   -   -   -   64:9e:f3:be:db:66   00:16:3c:f1:fd:6d   GB  -   -   51.4964 -0.1224 US  VA  Ashburn 39.0469 -77.4903    1:c/CLmyk4xRElyzleEMhJ4Baf4Gk=
1637156308.073703   C5VoWQz890uMGQ80i   50.18.18.14 8   198.71.247.91   0   -   -   icmp    -   0.000049    8   8   OTH -   -   0   -   1   36  1   36  -   -   -   64:9e:f3:be:db:66   00:16:3c:f1:fd:6d   US  CA  San Jose    37.3388 -121.8916   US  VA  Ashburn 39.0469 -77.4903    1:g6Ic2EGNte5The4ZgH83QyBviYM=
1637156441.248995   CoMNrWaF6TCc5v9F    198.71.247.91   53590   91.189.89.199   123 -   -   udp ntp 0.132333    48  48  SF  -   -   0   Dd  1   76  1   76  -   -   -   00:16:3c:f1:fd:6d   64:9e:f3:be:db:66   US  VA  Ashburn 39.0469 -77.4903    GB  ENG London  51.5095 -0.0955 1:+5Mf6akMTGuI1eAX/a9DaKSLPa8=
1637156614.649079   Co2oVs4STLavNDKQwf  54.176.143.72   8   198.71.247.91   0   -   -   icmp    -   0.000131    8   8   OTH -   -   0   -   1   36  1   36  -   -   -   64:9e:f3:be:db:66   00:16:3c:f1:fd:6d   US  CA  San Jose    37.3388 -121.8916   US  VA  Ashburn 39.0469 -77.4903    1:WHH+8OuOygRPi50vrH45p9WwgA4=
1637156703.913656   C1UE8M1G6ok0h7OrJi  35.87.13.125    8   198.71.247.91   0   -   -   icmp    -   0.000058    8   8   OTH -   -   0   -   1   36  1   36  -   -   -   64:9e:f3:be:db:66   00:16:3c:f1:fd:6d   US  OR  Boardman    45.8234 -119.7257   US  VA  Ashburn 39.0469 -77.4903    1:A+WZZOx8yg3UCoNV1IeiSNZUxEk=

Step 1: Parse the input​

We first need to parse the log file into structured form that we can work with the individual fields. Thanks to Tenzir's Zeek support, we can get quickly turn TSV logs into structured data using a single operator:

conn-to-ocsf.tql
read_zeek_tsv

Run the pipeline as follows:

tenzir --tql2 -f conn-to-ocsf.tql < conn.log
Output
{
  "ts": "2021-11-17T13:32:43.237881",
  "uid": "CZwqhx3td8eTfCSwJb",
  "id": {
    "orig_h": "128.14.134.170",
    "orig_p": 57468,
    "resp_h": "198.71.247.91",
    "resp_p": 80
  },
  "proto": "tcp",
  "service": "http",
  "duration": "5.16s",
  "orig_bytes": 205,
  "resp_bytes": 278,
  "conn_state": "SF",
  "local_orig": null,
  "local_resp": null,
  "missed_bytes": 0,
  "history": "ShADadfF",
  "orig_pkts": 6,
  "orig_ip_bytes": 525,
  "resp_pkts": 5,
  "resp_ip_bytes": 546,
  "tunnel_parents": null,
  "community_id": "1:YXWfTYEyYLKVv5Ge4WqijUnKTrM=",
  "_write_ts": null
}
{
  "ts": "2021-11-17T13:38:28.073703",
  "uid": "C5VoWQz890uMGQ80i",
  "id": {
    "orig_h": "50.18.18.14",
    "orig_p": 8,
    "resp_h": "198.71.247.91",
    "resp_p": 0
  },
  "proto": "icmp",
  "service": null,
  "duration": "49.0us",
  "orig_bytes": 8,
  "resp_bytes": 8,
  "conn_state": "OTH",
  "local_orig": null,
  "local_resp": null,
  "missed_bytes": 0,
  "history": null,
  "orig_pkts": 1,
  "orig_ip_bytes": 36,
  "resp_pkts": 1,
  "resp_ip_bytes": 36,
  "tunnel_parents": null,
  "community_id": "1:g6Ic2EGNte5The4ZgH83QyBviYM=",
  "_write_ts": null
}
{
  "ts": "2021-11-17T13:40:41.248995",
  "uid": "CoMNrWaF6TCc5v9F",
  "id": {
    "orig_h": "198.71.247.91",
    "orig_p": 53590,
    "resp_h": "91.189.89.199",
    "resp_p": 123
  },
  "proto": "udp",
  "service": "ntp",
  "duration": "132.33ms",
  "orig_bytes": 48,
  "resp_bytes": 48,
  "conn_state": "SF",
  "local_orig": null,
  "local_resp": null,
  "missed_bytes": 0,
  "history": "Dd",
  "orig_pkts": 1,
  "orig_ip_bytes": 76,
  "resp_pkts": 1,
  "resp_ip_bytes": 76,
  "tunnel_parents": null,
  "community_id": "1:+5Mf6akMTGuI1eAX/a9DaKSLPa8=",
  "_write_ts": null
}
{
  "ts": "2021-11-17T13:43:34.649079",
  "uid": "Co2oVs4STLavNDKQwf",
  "id": {
    "orig_h": "54.176.143.72",
    "orig_p": 8,
    "resp_h": "198.71.247.91",
    "resp_p": 0
  },
  "proto": "icmp",
  "service": null,
  "duration": "131.0us",
  "orig_bytes": 8,
  "resp_bytes": 8,
  "conn_state": "OTH",
  "local_orig": null,
  "local_resp": null,
  "missed_bytes": 0,
  "history": null,
  "orig_pkts": 1,
  "orig_ip_bytes": 36,
  "resp_pkts": 1,
  "resp_ip_bytes": 36,
  "tunnel_parents": null,
  "community_id": "1:WHH+8OuOygRPi50vrH45p9WwgA4=",
  "_write_ts": null
}
{
  "ts": "2021-11-17T13:45:03.913656",
  "uid": "C1UE8M1G6ok0h7OrJi",
  "id": {
    "orig_h": "35.87.13.125",
    "orig_p": 8,
    "resp_h": "198.71.247.91",
    "resp_p": 0
  },
  "proto": "icmp",
  "service": null,
  "duration": "58.0us",
  "orig_bytes": 8,
  "resp_bytes": 8,
  "conn_state": "OTH",
  "local_orig": null,
  "local_resp": null,
  "missed_bytes": 0,
  "history": null,
  "orig_pkts": 1,
  "orig_ip_bytes": 36,
  "resp_pkts": 1,
  "resp_ip_bytes": 36,
  "tunnel_parents": null,
  "community_id": "1:A+WZZOx8yg3UCoNV1IeiSNZUxEk=",
  "_write_ts": null
}

Step 2: Map to OCSF​

Now that we have structured data to work with, our objective is to map the fields from the Zeek conn.log to OCSF. The corresponding event class in OCSF is Network Activity. We will be using OCSF v1.3.0 throughout this tutorial.

To make the mapping process more organized, we map per attribute group:

  1. Classification: Important for the taxonomy and schema itself
  2. Occurrence: Temporal characteristics about when the event happened
  3. Context: Temporal characteristics about when the event happened
  4. Primary: Defines the key semantics of the given event

Within each attribute group, we go through the attributes in the order of the three requirement flags required, recommended, and optional.

Here's a template for the mapping pipeline:

// (1) Move original event into dedicated field.
this = { event: this }
// (2) Assign some intermediate values for use in the next step, e.g., because
//     they're used multiple times.
class_uid = 4001
activity_id = 6
...
// (3) Populate the OCSF event.
this = {
  // --- Classification ---
  activity_id: activity_id,
  class_uid: class_uid,
  type_uid: class_uid * 100 + activity_id,
  ...
  // --- Occurrence ---
  ...
  // --- Context ---
  unmapped: event, // (4) Explicitly assign unmapped.
  ...
  // --- Primary ---
  ...
}
// (5) Drop all mapped fields, with the effect that the remaining fields remain
//     in unmapped.
drop(
  unmapped.id,
  ...
)
// (6) Assign a new schema name to the transformed event.
@name = "ocsf.network_activity"

Let's unpack this:

  1. With this = { event: this } we move the original event into the field event. This also has the benefit that we avoid name clashes when creating new fields in the next steps.
  2. There are several fields we want to reference in expressions in the subsequent assignment, so we precompute them here.
  3. The giant this = { ... } assignment create the OCSF event, with a field order that matches the official OCSF documentation.
  4. We copy the original event into unmapped.
  5. After we mapped all fields, we now explicitly remove them from unmapped. This has the effect that everything we didn't touch automatically ends up here.
  6. We give the event a new schema name so that we can easily filter by its shape in further Tenzir pipelines.

Now that we know the general structure, let's get our hands dirty and go deep into the actual mapping.

Classification Attributes​

The classification attributes are important for the schema. Mapping them is pretty mechanical and mostly involves going through the docs.

class_uid = 4001
activity_id = 6
activity_name = "Traffic"
this = {
  // --- Classification (required) ---
  activity_id: activity_id,
  category_uid: 4,
  class_uid: class_uid,
  type_uid: class_uid * 100 + activity_id,
  severity_id: 1,
  // --- Classification (optional) ---
  activity_name: activity_name,
  category_name: "Network Activity",
  class_name: "Network Activity",
  severity: "Informational",
}
// TODO: provide a function for this and make it possible to reference
// `type_uid` from the same assignment.
//type_name: ocsf_type_name(type_uid),

Because we want to compute the type_uid as class_uid * 100 activity_id, we assign these variables in dedicated fields beforehand.

Occurrence Attributes​

Let's tackle the next group: Occurrence. These attributes are all about time. We won't repeat the above record fields in the assignment, but the idea is to incrementally construct a giant statement with the assignment this = { ... }:

this = {
  // --- Classification ---
  ...
  // --- Occurrence (required) ---
  time: event.ts,
  // --- Occurrence (recommended) ---
  // TODO: provide a function for this.
  // timezone_offset: ...
  // --- Occurrence (optional) ---
  duration: event.duration,
  end_time: event.ts + event.duration,
  start_time: event.ts,
}

Context Attributes​

The Context attributes provide enhancing information. Most notably, the metadata attribute holds data-source specific information and the unmapped attribute collects all fields that we cannot map directly to their semantic counterparts in OCSF.

this = {
  // --- Classification, Occurrence ---
  ...
  // --- Context (required) ---
  metadata: {
    log_name: "conn", // Zeek calls it "path"
    logged_time: event._write_ts,
    product: {
      name: "Zeek",
      vendor_name: "Zeek",
    },
    uid: event.uid,
    version: "1.3.0",
  },
  // --- Context (optional) ---
  unmapped: event
}

Note that we're copying the original event into unmapped so that we can in a later step remove all mapped fields from it.

Primary Attributes​

The primary attributes define the semantics of the event class itself. This is where the core value of the data is, as we are mapping the most event-specific information.

For this, we still need to precompute several attributes that we are going to use in the this = { ... } assignment. You can see the use of if/else here to create a constant field based on values in the original event.

if event.local_orig and event.local_resp {
  direction = "Lateral"
  direction_id = 3
} else if event.local_orig {
  direction = "Outbound"
  direction_id = 2
} else if event.local_resp {
  direction = "Inbound"
  direction_id = 1
} else {
  direction = "Unknown"
  direction_id = 0
}
if event.proto == "tcp" {
  protocol_num = 6
} else if event.proto == "udp" {
  protocol_num = 17
} else if event.proto == "icmp" {
  protocol_num = 1
} else {
  protocol_num = -1
}
if event.id.orig_h.is_v6() or event.id.resp_h.is_v6() {
  protocol_ver_id = 6
} else {
  protocol_ver_id = 4
}
this = {
  // --- Classification, Occurrence, Context ---
  ...
  // --- Primary (required) ---
  dst_endpoint: {
    ip: event.id.resp_h,
    port: event.id.resp_p,
    // TODO: start a conversation in the OCSF Slack to figure out how to
    // assign the entire connection a protocol. We use svc_name as the
    // next best thing, but it clearly can't be different between
    // endpoints for the service semantics that Zeek has.
    svc_name: event.service,
  },
  // --- Primary (recommended) ---
  connection_info: {
    uid: event.community_id,
    direction: direction,
    direction_id: direction_id,
    protocol_ver_id: protocol_ver_id,
    protocol_name: event.proto,
    protocol_num: protocol_num,
  },
  src_endpoint: {
    ip: event.id.orig_h,
    port: event.id.orig_p,
    svc_name: event.service,
  },
  // TODO: we actually could go deeper into the `conn_state` field and
  // choose a more accurate status. But this would require string
  // manipulations and/or regex matching, which TQL doesn't have yet.
  status: "Other",
  status_code: event.conn_state,
  status_id: 99,
  traffic: {
    bytes_in: event.resp_bytes,
    bytes_out: event.orig_bytes,
    packets_in: event.resp_pkts,
    packets_out: event.orig_pkts,
    total_bytes: event.orig_bytes + event.resp_bytes,
    total_packets: event.orig_pkts + event.resp_pkts,
  },
  // --- Primary (optional) ---
  // TODO
  // - `ja4_fingerprint_list`: once we have some sample logs with JA4
  //   fingerprints, which requires an additional Zeek package, we should
  //   populate them here.
  // - `tls`: if we buffer ssl log for this connection, we could add the
  //   information in here.
}

Recap​

Phew, that's a wrap. Here's the entire pipeline in a single piece:

Complete pipeline definition
conn-to-ocsf.tql
// tql2
read_zeek_tsv
where @name == "zeek.conn"
this = { event: this }
class_uid = 4001
activity_id = 6
activity_name = "Traffic"
if event.local_orig and event.local_resp {
  direction = "Lateral"
  direction_id = 3
} else if event.local_orig {
  direction = "Outbound"
  direction_id = 2
} else if event.local_resp {
  direction = "Inbound"
  direction_id = 1
} else {
  direction = "Unknown"
  direction_id = 0
}
if event.proto == "tcp" {
  protocol_num = 6
} else if event.proto == "udp" {
  protocol_num = 17
} else if event.proto == "icmp" {
  protocol_num = 1
} else {
  protocol_num = -1
}
if event.id.orig_h.is_v6() or event.id.resp_h.is_v6() {
  protocol_ver_id = 6
} else {
  protocol_ver_id = 4
}
this = {
  // --- Classification (required) ---
  activity_id: activity_id,
  category_uid: 4,
  class_uid: class_uid,
  type_uid: class_uid * 100 + activity_id,
  severity_id: 1,
  // --- Classification (optional) ---
  activity_name: activity_name,
  category_name: "Network Activity",
  class_name: "Network Activity",
  severity: "Informational",
  // TODO: provide a function for this and make it possible to reference
  // `type_uid` from the same assignment.
  //type_name: ocsf_type_name(type_uid),
  // --- Occurrence (required) ---
  time: event.ts,
  // --- Occurrence (recommended) ---
  // TODO: provide a function for this
  //timezone_offset: ..
  // --- Occurrence (optional) ---
  duration: event.duration,
  end_time: event.ts + event.duration,
  start_time: event.ts,
  // --- Context (required) ---
  metadata: {
    log_name: "conn", // Zeek calls it "path"
    logged_time: event._write_ts,
    product: {
      name: "Zeek",
      vendor_name: "Zeek",
    },
    uid: event.uid,
    version: "1.3.0",
  },
  // --- Context (optional) ---
  unmapped: event,
  // --- Primary (required) ---
  dst_endpoint: {
    ip: event.id.resp_h,
    port: event.id.resp_p,
    // TODO: start a conversation in the OCSF Slack to figure out how to
    // assign the entire connection a protocol. We use svc_name as the
    // next best thing, but it clearly can't be different between
    // endpoints for the service semantics that Zeek has.
    svc_name: event.service,
  },
  // --- Primary (recommended) ---
  connection_info: {
    uid: event.community_id,
    direction: direction,
    direction_id: direction_id,
    protocol_ver_id: protocol_ver_id,
    protocol_name: event.proto,
    protocol_num: protocol_num,
  },
  src_endpoint: {
    ip: event.id.orig_h,
    port: event.id.orig_p,
    svc_name: event.service,
  },
  // TODO: we actually could go deeper into the `conn_state` field and
  // choose a more accurate status. But this would require string
  // manipulations and/or regex matching, which TQL doesn't have yet.
  status: "Other",
  status_code: event.conn_state,
  status_id: 99,
  traffic: {
    bytes_in: event.resp_bytes,
    bytes_out: event.orig_bytes,
    packets_in: event.resp_pkts,
    packets_out: event.orig_pkts,
    total_bytes: event.orig_bytes + event.resp_bytes,
    total_packets: event.orig_pkts + event.resp_pkts,
  },
  // --- Primary (optional) ---
  // TODO
  // - `ja4_fingerprint_list`: once we have some sample logs with JA4
  //   fingerprints, which requires an additional Zeek package, we should
  //   populate them here.
  // - `tls`: if we buffer ssl log for this connection, we could add the
  //   information in here.
}
// Drop all the mapped fields.
drop(
  unmapped._write_ts,
  unmapped.community_id,
  unmapped.conn_state,
  unmapped.duration,
  unmapped.id,
  unmapped.local_orig,
  unmapped.local_resp,
  unmapped.orig_bytes,
  unmapped.orig_pkts,
  unmapped.proto,
  unmapped.resp_bytes,
  unmapped.resp_pkts,
  unmapped.service,
  unmapped.ts,
  unmapped.uid,
)
@name = "ocsf.network_activity"

Let's run the pipeline:

tenzir --tql2 -f conn-to-ocsf.tql < conn.log

You should get the following output:

{
  "activity_id": 6,
  "category_uid": 4,
  "class_uid": 4001,
  "metadata": {
    "log_name": "conn",
    "logged_time": null,
    "product": {
      "name": "Zeek",
      "vendor_name": "Zeek"
    },
    "uid": "C1UE8M1G6ok0h7OrJi",
    "version": "1.3.0"
  },
  "time": "2021-11-17T13:45:03.913656",
  "type_uid": 400106,
  "status": "Other",
  "status_code": "OTH",
  "status_id": 99,
  "activity_name": "Traffic",
  "category_name": "Network Activity",
  "class_name": "Network Activity",
  "duration": "58.0us",
  "end_time": "2021-11-17T13:45:03.913714",
  "start_time": "2021-11-17T13:45:03.913656",
  "unmapped": {
    "history": null,
    "missed_bytes": 0,
    "orig_ip_bytes": 36,
    "resp_ip_bytes": 36,
    "tunnel_parents": null
  },
  "dst_endpoint": {
    "ip": "198.71.247.91",
    "port": 0,
    "svc_name": null
  },
  "connection_info": {
    "uid": "1:A+WZZOx8yg3UCoNV1IeiSNZUxEk=",
    "direction": "Unknown",
    "direction_id": 0,
    "protocol_ver_id": 4,
    "protocol_name": "icmp",
    "protocol_num": 1
  },
  "src_endpoint": {
    "ip": "35.87.13.125",
    "port": 8,
    "svc_name": null
  },
  "traffic": {
    "bytes_in": 8,
    "bytes_out": 8,
    "packets_in": 1,
    "packets_out": 1,
    "total_bytes": 16,
    "total_packets": 2
  }
}

Step 3: Combine multiple pipelines​

So far we've mapped just a single event. But Zeek has dozens of different event types, and we need to write one mapping pipeline for each. But how do we combine the individual pipelines?

Tenzir's answer for this is topic-based publish-subscribe. The publish and subscribe operators send events to, and read events from a topic, respectively. Here's an illustration of the conceptual approach we are going to use:

The first pipeline publishes to the zeek topic:

read_zeek_tsv
publish "zeek"

Then we have one pipeline per Zeek event type X that publishes to the ocsf topic:

subscribe "zeek"
where @name == "zeek.X"
// map to OCSF
publish "ocsf"

The idea is that all Zeek logs arrive at the topic zeek, and all mapping pipelines start there by subscribing to the same topic, but each pipeline filters out one event type. Finally, all mapping pipelines publish to the ocsf topic that represents the combined feed of all OCSF events. Users can then use the same filtering pattern as with Zeek to get a subset of the OCSF stream, e.g., subscribe "ocsf" | where @name == "ocsf.authentication" for all OCSF Authentication events.

Isn't this inefficient?

You may think that copying the full feed of the zeek topic to every mapping pipeline is inefficient. The good news is that it is not, for two reasons:

  1. Data transfers between publish and subscribe use the same zero-copy mechanism that pipelines use internally for sharing of events.
  2. Pipelines of the form subscribe ... | where <predicate> push perform predicate pushdown and send predicate upstream so that the filtering can happen as early as possible.

Summary​

In this tutorial, we demonstrated how you map logs to OCSF event classes. We used the Zeek network monitor as a case study to illustrate the general mapping pattern. Finally, we explained how to use Tenzir's pub-sub mechanism to scale from on to many pipelines, each of which handle a specific OCSF event class.