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Match Threat Intelligence

Commercial Plugin

This feature is available as commercial plugin that runs on top open-source VAST. Please contact us if you'd like to try it out.

Terminology: Threat Intelligence

Threat intelligence is security content that describes threats from various perspectives. Practitioners typically distinguish strategic, operational, and tactical threat intelligence. We focus on the tactical data that decomposes into observables as singular data points, or more specifically indicators of compromise (IoCs) that reflect malicious activity.

VAST can live-match threat intelligence against the incoming stream of events, producing an alert feed of sightings. This feature fits into the bigger theme of a unified detection strategy with a security-content-driven workflow.

VAST features matchers that check whether specific field values exist in dynamic set of indicators. A successful match emits a sighting as output. This functionality resembles Suricata's datasets or Zeek's intel framework, but generalized to all security telemetry. Key matcher features include:

  • Exact & Fuzzy Mode: controllable memory usage through multiple storage backends, such as hash tables, Bloom filters, and Cuckoo filters.

  • Surgical Target Locking: fine-grained configuration options to dispatch matchers to fields in the data, fully leveraging VAST's type system.

  • Composable Sighting Streams: mix-and-match sighting streams to combine the results of matchers, e.g., fuse TLP:RED and inhouse indicators in one stream and OSINT and TLP:WHITE in a another one.

  • Full Control: flexible controls to add/remove indicators, perform bulk-imports, and save/restore binary matcher state.

Working with matchers involves three separate steps:

  1. Start a matcher
  2. Add/remove indicators
  3. Attach to the matcher to consume sightings

VAST uniquely identifies matchers by their name, either as specified in the YAML configuration or on the command line. Whenever interacting with a matcher, you need to pass the name as argument to all operations. The general pattern looks as follows:

vast matcher <command> [options] <name>

VAST also supports executing operations on multiple matchers at once, e.g., to a add an indicator to a many matchers. To this end, simply use a comma-separated list for the positional name argument, e.g., vast matcher add a,b,c ... to act on matchers a, b, and c.


To use matchers, make sure that your VAST distribution has the matcher plugin available, e.g., by checking the output of vast version:

vast -q --plugins=all version | jq .plugins.matcher

Start Matchers

There exist two methods to start matchers:

  1. Server-side: configure them in the vast.yaml configuration
  2. Client-side: invoke vast matcher start on the command line

Method (1) produces persistent matchers that survive restarts and flush their state periodically; (2) produces ephemeral matchers, which are functionally equivalent but require manual state management if persistence is desired.

A matcher operates in a specific mode. Please consult the section matcher modes below to understand the trade-offs.


The configuration key plugins.matcher contains the configuration for persistent matchers, i.e., those that survice restarts and get periodically persisted.

Here is an example configuration snippet:

# The amount of time to wait before triggering a write to disk for matchers
# "dirty" matchers, i.e., those that have been modified since the last
# write.
persistence-interval: 30 mins
# VAST automatically starts all matchers configured in this section.
# An exact matcher that operates on fields.
mode: exact
- net.domain
- net.hostname
# A Cuckoo matcher that operates on all fields of type IP.
mode: cuckoo
- ip
# A DCSO bloom matcher that operates on all fields of type string
mode: dcso-bloom
- string
capacity: 1000000
false-positive-probability: 0.001

Adding a matcher means adding a new entry under the key matchers.

The matcher-global option persistence-interval controls how fast a persist operation takes place after a state mutation. Regardless of the configured value, VAST persists all matchers with pending modifications on shutdown.


When deploying matchers, editing the server-side configuration can be unwieldy and result int undesired blind spots, because they require a restart of the server for the configuration changes to take effect. This is why VAST also supports spawning ephemeral matchers via the CLI. Ephemeral matchers behave exactly like persistent matchers, with the only difference that the VAST server doesn't manage their state. However, it is still possible to manually save/load the matcher state.

To start an ephemeral matcher, use vast matcher start. The command line options are identical to the YAML keys. For example, to spawn the iocs matcher configured above as ephemeral matcher, use this command:

vast matcher start \
--mode=dcso-bloom \
--capacity=1000000 \
--false-positive-probability=0.001 \
--match-types=string \

List Matchers

To show the running matchers, use vast matcher list. Example output may look like this:

hostnames (disabled with 0 clients)
strings (disabled with 0 clients)
ips (disabled with 0 clients)

Matchers are enabled when one or more clients are attached. See the next section on how to attach to a matcher.

Attach to Matchers

Unless you attach to a matcher, it will not hook into the ingress event stream in order to conserve resources. A matcher is enabled if it has at least one connected client.

To attach to a matcher, you need to specified output format and the matcher name:

vast matcher attach csv hostnames

The process will block and print all sightings in CSV format on standard output, until it receives a termination signal, e.g., by pressing CTRL+C or sending it SIGINT.

In the common case, you don't want to repeat this for every matcher. To attach to multiple matchers with a single client, provide their names as list:

vast matcher attach json hostnames,ips,iocs

You will now receive sightings from all matchers in JSON format. There is no ordering guarantee on the sighting output, as VAST fuses the sighting stream asynchronously to deliver optimal latency.

Add Indicators

There exist two methods to populate matchers with content:

  1. Add a single indicator
  2. Bulk-import a set of indicators

One-shot Import

Adding a single indicator involves passing it on the command line:

vast matcher add <matcher> <value> [context]

For example, to add and IP address along with an opaque identifier to the matcher ips, use:

vast matcher add ips opaque-id-42

The context value opaque-id-42 will show in in all sightings for this indicator, e.g., to associate it with an external unique ID.

Context Usability

The context argument is only supported by the exact matcher. Probabilistic matchers cannot store the extra context data. Please consult the section on matcher modes to understand the inherent trade-offs.

Bulk Import

Adding large sets of indicators using vast matcher add does not scale, because the overhead of establishing a connection to the server dwarfs the time it takes to implant the indicator into the corresponding data structure. To import large sets of indicators in bulk, use the vast matcher import command.

The vast matcher import command mirrors the interface of the vast import command. Instead of importing events into the database, it imports events containing indicators and forwards them to selected matchers. Let's take a look at an example incovation using the Pulsedive threat intelligence feed:

# Pulsedive feed without retired indicators and restricted to ip and ipv6 IoCs.

# Ingest the feed into the matcher 'ips' we created above.
curl -sSL "$feed_url" |
vast matcher import -t pulsedive csv ips

The curl command downloads a CSV and dumps it to STDOUT. The vast matcher import command reads CSV content by specifying csv as first positional argument. We are also telling VAST via -t pulsedive that the data matches the pulsedive type (specified in the bundled pulsedive.schema). After parsing, VAST forwards the parsed indicators to the matcher ips.

An additional concepts definition for the matcher.indicator.value and matcher.indicator.context fields for the pulsedive type lets the command know which fields to treat as value and optional context.

The above example used a pre-filtered list from Pulsedive. However, import filter expressions allow for doing the filtering on the fly using VAST's regular import filter expressions like this:

# The full Pulsedive feed.

# Ingest the feed into the matcher 'ips', but skip all retired indicators.
curl -sSL "${feed_url}" |
vast matcher import -t pulsedive csv ips \
'risk != /:retired/ && type == /ip.*/

The matcher plugin conveniently ships with a Pulsedive schema and concept definitions for use with the matcher plugin in <sysconfdir>/share/vast/plugins/matcher/schema.

Delete Indicators

The remove command is the dual to add: it removes a single indicator value. For example, to remove from the matcher ips, invoke:

vast matcher remove ips
Context Usability

Not all matchers support deletion of indicators. Please consult the section on matcher modes to understand the inherent trade-offs. :::

Bulk deletion is currently not possible, but you can manage the matcher state manually, e.g., to externally constructed reload Bloom filters.

Manage Matcher State

To simplify managing of large sets of indicators for operators, VAST supports client-side modification of the underlying raw matcher state.

For example, this allows you to compile a Bloom filter containing several millions of indicators in your threat intelligence platform and synchronize the content for matching in VAST. Another use case involves dumping matcher state to replicate the matcher at another VAST instance.

Save/load state at the client

To show the state of a specific matcher, use the matcher save command:

vast matcher save ips > ips.state

The command writes the binary state of the matcher ips to standard output, expecting users to redirect it according to their needs. The state is portable, and you can copy it over to other machines as well.

To replace the state of a running matcher, use the matcher load command:

vast matcher load ips < ips.state

The command reads the binary state from standard input.

You can also combine save and load to migrate the state of one matcher, e.g., to perform a modification that you want to reverse later on, or to "fork" a matcher. To migrate matcher foo to matcher bar, use:

vast matcher save foo | vast matcher load bar


Understand Matcher Modes

Fundamentally, a matcher maintains set of indicators. The mode controls how the matcher stores the indicator data. The table below gives a quick summary about the trade-offs when choosing a mode:

Cuckoocuckoo✔︎✔︎O((log(1/p) + 2) / load)
DCSO bloomdcso-bloom✔︎O(1.44 log(1/p))


The exact mode maintains a key-value mapping in the form of a hash map using robin hood hashing. Every key in the table represents the indicator item. The value is optional context that can be chosen freely.

The exact matcher supports all operations, at the cost of growing linearly with the number of indicators.


The cuckoo mode summarizes the set of indicators in a Cuckoo filter.

Compared to Bloom filters, Cuckoo filters have the following advantages:

  • Support for deleting previously inserted elements
  • Better false-positive probability as the filter load increases
  • Smaller memory footprint for false-positive probabilities less than 3%.
  • O(1) vs O(k) operations, where k is the number of hash functions in the Bloom filter
Deleting Elements

The delete operation comes with a caveat: it is only well-defined if the to-be-deleted item has been previously added. Otherwise the filter enters an undefined state and can produce false negatives in addition to false positives. VAST cannot enforce this pre-condition, so you must tread carefully when using it.

The Cuckoo filter is currently not parameterizable. The size is always 128 MiB. In the future, we will offer the same tuning knobs as the DCSO Bloom filter below.

DCSO Bloom

The dcso-bloom mode stores the indicators in a Bloom filter.

The two tuning knobs are capacity (maximum number of items in the filter) and false-positive probability (chance of reporting an indicator not in the filter). The two parameters dictate the space usage. Please consult Thomas Hurst's Bloom Filter Calculator for finding the optimal configuration for your use case.

The Bloom ftiler is complete C++ rebuild of DCSO's Bloom filter library bloom. VAST's implementation is binary-compatible and uses the exact same method for FNV1 hashing and parameter calculation, making it a drop-in replacement for bloom users.

Constructing a bloom matcher

To construct a dcso-bloom matcher, use matcher start. The additional parameters --false-positive-probability (-n) and --capacity (-n) allow for controlling the underlying Bloom filter:

vast matcher start --mode=dcso-bloom -p 0.1 -n 100 --match-fields=net.domain ns
vast matcher add ns
vast matcher add ns

Importing bloom-generated binary filters

In addition to controlling matcher content using the matcher add and matcher import commands, you can provide a binary Bloom filter created by the Go utility bloom:

bloom create -p 0.1 -n 100 ns.bloom
echo, | bloom -s insert ns.bloom

Use bloom show ns.bloom to display a few statistics about the filter, such as the number of elements, false-positive probability, number of hash functions, and bits used.

Finally, we hand the Bloom filter over to VAST and associate it with the matcher called ns:

vast matcher load dns < ns.bloom

See the section on matcher state management for a more detailed discussion on loading binary state into the matcher.


This section includes real-world examples to illustrate how the matcher works in practice.

IP Blocklists

IP blocklists make up for a large share of low-level IoCs, often to represent attacker infrastructure, such as C2 servers.

The Feodo Tracker from represents one such blocklist that gets updated every 5 minutes. Let's take a look:

head -n 15 ipblocklist.csv
# Feodo Tracker Botnet C2 IP Blocklist (CSV) #
# Last updated: 2021-08-17 15:00:42 UTC #
# #
# Terms Of Use: #
# For questions please contact feodotracker [at] #
"2021-01-17 07:30:05","","443","offline","2021-08-18","Dridex"
"2021-01-17 07:44:46","","4643","online","2021-08-18","Dridex"
"2021-01-17 07:44:50","","3786","online","2021-08-18","Dridex"
"2021-01-17 07:45:55","","3388","online","2021-08-18","Dridex"
"2021-01-17 07:45:58","","33443","online","2021-08-18","Dridex"
"2021-01-17 07:47:59","","4643","online","2021-08-18","Dridex"

Before VAST can read this data, we need to tell VAST what type to use for it. We write an abuse module for this:

module: abuse
- first_seen_utc: time
- dst_ip: ip
- dst_port: port
- c2_status:
- online
- offline
- last_online: time
- malware: string

In addition, you need to tell VAST what fields have the indicator data, consisting of value and an optional context. To this end, you need to provide a concept definition:

- abuse.feodo.blocklist.dst_ip
- abuse.feodo.blocklist.malware

Now we can translate the blocklist into a format that VAST can read, e.g., CSV or JSON. In the example below, we simply add a header to the plain text file to create a valid CSV. (Feodo also provides a CSV download, but we want to illustrate how you can easily perform a translation.)

curl -sSL |
tr -d '\015' |
grep -v '^#' |
vast matcher import -t feodo.blocklist csv ips

We throw in a tr -d '\015' to convert DOS linebreaks to UNIX and strip # comments via grep -v.

The live matcher ips is now armed with the Feodo blocklist and matches it on all IP addresses.