Unusual Process Spawned by a Host
A machine learning job has detected a suspicious Windows process. This process has been classified as suspicious in two ways. It was predicted to be suspicious by the ProblemChild supervised ML model, and it was found to be an unusual process, on a host that does not commonly manifest malicious activity. Such a process may be an instance of suspicious or malicious activity, possibly involving LOLbins, that may be resistant to detection using conventional search rules.
Elastic rule (View on GitHub)
1[metadata]
2creation_date = "2023/09/19"
3integration = ["problemchild", "endpoint", "windows"]
4maturity = "production"
5min_stack_comments = "LotL package job ID and rule removal updates"
6min_stack_version = "8.9.0"
7updated_date = "2024/04/01"
8
9[rule]
10anomaly_threshold = 75
11author = ["Elastic"]
12description = """
13A machine learning job has detected a suspicious Windows process. This process has been classified as suspicious in two
14ways. It was predicted to be suspicious by the ProblemChild supervised ML model, and it was found to be an unusual
15process, on a host that does not commonly manifest malicious activity. Such a process may be an instance of suspicious
16or malicious activity, possibly involving LOLbins, that may be resistant to detection using conventional search rules.
17"""
18from = "now-45m"
19interval = "15m"
20license = "Elastic License v2"
21machine_learning_job_id = "problem_child_rare_process_by_host"
22name = "Unusual Process Spawned by a Host"
23setup = """## Setup
24
25The rule requires the Living off the Land (LotL) Attack Detection integration assets to be installed, as well as Windows process events collected by integrations such as Elastic Defend or Winlogbeat.
26
27### LotL Attack Detection Setup
28The LotL Attack Detection integration detects living-off-the-land activity in Windows process events.
29
30#### Prerequisite Requirements:
31- Fleet is required for LotL Attack Detection.
32- To configure Fleet Server refer to the [documentation](https://www.elastic.co/guide/en/fleet/current/fleet-server.html).
33- Windows process events collected by the [Elastic Defend](https://docs.elastic.co/en/integrations/endpoint) integration or Winlogbeat(https://www.elastic.co/guide/en/beats/winlogbeat/current/_winlogbeat_overview.html).
34- To install Elastic Defend, refer to the [documentation](https://www.elastic.co/guide/en/security/current/install-endpoint.html).
35- To set up and run Winlogbeat, follow [this](https://www.elastic.co/guide/en/beats/winlogbeat/current/winlogbeat-installation-configuration.html) guide.
36
37#### The following steps should be executed to install assets associated with the LotL Attack Detection integration:
38- Go to the Kibana homepage. Under Management, click Integrations.
39- In the query bar, search for Living off the Land Attack Detection and select the integration to see more details about it.
40- Under Settings, click Install Living off the Land Attack Detection assets and follow the prompts to install the assets.
41
42#### Ingest Pipeline Setup
43**Before you can enable this rule**, you'll need to enrich Windows process events with predictions from the Supervised LotL Attack Detection model. This is done via the ingest pipeline named `<package_version>-problem_child_ingest_pipeline` installed with the LotL Attack Detection package.
44- If using an Elastic Beat such as Winlogbeat, add the LotL ingest pipeline to it by adding a simple configuration [setting](https://www.elastic.co/guide/en/elasticsearch/reference/current/ingest.html#pipelines-for-beats) to `winlogbeat.yml`.
45- If adding the LotL ingest pipeline to an existing pipeline, use a [pipeline processor](https://www.elastic.co/guide/en/elasticsearch/reference/current/pipeline-processor.html). For example, you can check if your winlogbeat or Elastic Defend (the [default index pattern](https://docs.elastic.co/en/integrations/endpoint#logs) being `logs-endpoint*`) already has an ingest pipeline by navigating to `Data > Index Management`, finding the index (sometimes you need to toggle "Include hidden indices"), and checking the index's settings for a default or final [pipeline](https://www.elastic.co/guide/en/elasticsearch/reference/current/ingest.html#set-default-pipeline).
46
47#### Adding Custom Mappings
48- Go to the Kibana homepage. Under Management, click Stack Management.
49- Under Data click Index Management and navigate to the Component Templates tab.
50- Templates that can be edited to add custom components will be marked with a @custom suffix. Edit the @custom component template corresponding to the beat/integration you added the LotL ingest pipeline to, by pasting the following JSON blob in the "Load JSON" flyout:
{ "properties": { "problemchild": { "properties": { "prediction": { "type": "long" }, "prediction_probability": { "type": "float" } } }, "blocklist_label": { "type": "long" } } }
1
2### Anomaly Detection Setup
3**Before you can enable this rule**, you'll need to enable the corresponding Anomaly Detection job.
4- Go to the Kibana homepage. Under Analytics, click Machine Learning.
5- Under Anomaly Detection, click Jobs, and then click "Create job". Select the Data View containing your enriched Windows process events. For example, this would be `logs-endpoint.events.*` if you used Elastic Defend to collect events, or `winlogbeat-*` if you used Winlogbeat.
6- If the selected Data View contains events that match the query in [this](https://github.com/elastic/integrations/blob/main/packages/problemchild/kibana/ml_module/problemchild-ml.json) configuration file, you will see a card for "Living off the Land Attack Detection" under "Use preconfigured jobs". Warning: If the ingest pipeline hasn't run for some reason, such as no eligible data in winlogbeat has come in yet, _you won't be able to see this card yet_. If that is the case, try troubleshooting the ingest pipeline, and check whether any ProblemChild predictions have been generated.
7- Keep the default settings and click "Create jobs" to start the anomaly detection job and datafeed.
8"""
9references = [
10 "https://www.elastic.co/guide/en/security/current/prebuilt-ml-jobs.html",
11 "https://docs.elastic.co/en/integrations/problemchild",
12 "https://www.elastic.co/security-labs/detecting-living-off-the-land-attacks-with-new-elastic-integration",
13]
14risk_score = 21
15rule_id = "56004189-4e69-4a39-b4a9-195329d226e9"
16severity = "low"
17tags = [
18 "Domain: Endpoint",
19 "OS: Windows",
20 "Use Case: Living off the Land Attack Detection",
21 "Rule Type: ML",
22 "Rule Type: Machine Learning",
23 "Tactic: Defense Evasion",
24]
25type = "machine_learning"
26[[rule.threat]]
27framework = "MITRE ATT&CK"
28[[rule.threat.technique]]
29id = "T1218"
30name = "System Binary Proxy Execution"
31reference = "https://attack.mitre.org/techniques/T1218/"
32
33
34[rule.threat.tactic]
35id = "TA0005"
36name = "Defense Evasion"
37reference = "https://attack.mitre.org/tactics/TA0005/"
References
-
Prebuilt job referenceedit These anomaly detection jobs automatically detect file system and network anomalies on your hosts. They appear in the Anomaly Detection interface of the Elastic Security ...
Read More -
ML solution package to detect Living off the Land (LotL) attacks in your environment. Requires a Platinum subscription.
Read More -
We added a Living off the land (LotL) detection package to the Integrations app in Kibana. In a single click, you can install and start using the ProblemChild model and associated assets including anomaly detection configurations and detection rules.
Read More
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