
Converte regras de detecção Sigma em consultas OpenSearch Lucene e PPL, incluindo Monitor Rules de alerta e suporte a correlação para fluxos de trabalho de detecção em SIEM.
Este é o backend OpenSearch para pySigma. Ele fornece o pacote sigma.backends.opensearch com duas classes de backend:
OpensearchLuceneBackend - Converte regras Sigma para a sintaxe de consulta LuceneOpenSearchPPLBackend - Converte regras Sigma para consultas PPL (Piped Processing Language)O backend Lucene suporta os seguintes formatos de saída:
Este backend é atualmente mantido por:
Como as consultas baseadas em Lucene são muito semelhantes às consultas Lucene do Elasticsearch, a maior parte do código deste backend vem de pySigma-backend-elasticsearch.
Alterações específicas do Opensearch e formatos de saída são feitos neste backend (por exemplo, Monitor Rules).
O backend PPL (Piped Processing Language) foi implementado do zero para suportar a linguagem de consulta nativa do OpenSearch. O PPL fornece:
O backend PPL suporta totalmente regras de correlação Sigma, permitindo a detecção de cenários complexos com múltiplos eventos:
sigma convert \
-t opensearch \
-p ecs_windows \
-f monitor_rule \
/data/sigma/rules/windows/process_creation/proc_creation_win_whoami_priv.yml
sigma convert \
-t opensearch-ppl \
-p ecs_windows \
/data/sigma/rules/windows/process_creation/proc_creation_win_whoami_priv.yml
from sigma.backends.opensearch import OpensearchLuceneBackend
from sigma.pipelines.sysmon import sysmon_pipeline
from sigma.pipelines.elasticsearch.windows import ecs_windows
from sigma.collection import SigmaCollection
from sigma.processing.resolver import ProcessingPipelineResolver
# Create our pipeline resolver
piperesolver = ProcessingPipelineResolver()
# Add wanted pipelines
piperesolver.add_pipeline_class(ecs_windows())
piperesolver.add_pipeline_class(sysmon_pipeline())
# Create a single sorted and prioritzed pipeline
resolved_pipeline = piperesolver.resolve(piperesolver.pipelines)
# Instantiate backend, using our resolved pipeline
# and some backend parameter
backend = OpensearchLuceneBackend(resolved_pipeline, index_names=['logs-*-*', 'beats-*'], monitor_interval=10, monitor_interval_unit="MINUTES")
rules = SigmaCollection.from_yaml("""
title: Run Whoami Showing Privileges
id: 97a80ec7-0e2f-4d05-9ef4-65760e634f6b
status: experimental
description: Detects a whoami.exe executed with the /priv command line flag instructing the tool to show all current user privieleges. This is often used after a privilege escalation attempt.
references:
- https://docs.microsoft.com/en-us/windows-server/administration/windows-commands/whoami
author: Florian Roth
date: 2021/05/05
modified: 2022/05/13
tags:
- attack.privilege_escalation
- attack.discovery
- attack.t1033
logsource:
category: process_creation
product: windows
detection:
selection_img:
- Image|endswith: '\whoami.exe'
- OriginalFileName: 'whoami.exe'
selection_cli:
CommandLine|contains: '/priv'
condition: all of selection*
falsepositives:
- Administrative activity (rare lookups on current privileges)
level: high
""")
# Print converted rule in Lucene syntax
print("Lucene Result: \n" + "\n".join(backend.convert(rules)))
# Print converted rule ready for dsl syntax
print("DSL Result: \n" + json.dumps(backend.convert(rules, output_format="dsl_lucene")[0], indent=2))
# Generate a JSON structure to be imported as monitor rule
print("Monitor Rule Result: \n" + backend.convert(rules, output_format="monitor_rule"))
Resultado Lucene:
winlog.channel:Microsoft\-Windows\-Sysmon\/Operational AND (event.code:1 AND ((process.executable:*\\whoami.exe OR process.pe.original_file_name:whoami.exe) AND process.command_line:*\/priv*))
Resultado DSL:
{
"query": {
"bool": {
"must": [
{
"query_string": {
"query": "winlog.channel:Microsoft\\-Windows\\-Sysmon\\/Operational AND (event.code:1 AND (winlog.channel:Microsoft\\-Windows\\-Sysmon\\/Operational AND (event.code:1 AND ((process.executable:*\\\\whoami.exe OR process.pe.original_file_name:whoami.exe) AND process.command_line:*\\/priv*))))",
"analyze_wildcard": true
}
}
]
}
}
}
Resultado da Regra de Monitor:
{
"type": "monitor",
"name": "SIGMA - Run Whoami Showing Privileges",
"description": "Detects a whoami.exe executed with the /priv command line flag instructing the tool to show all current user privieleges. This is often used after a privilege escalation attempt.",
"enabled": true,
"schedule": {
"period": {
"interval": 10,
"unit": "MINUTES"
}
},
"inputs": [
{
"search": {
"indices": [
"logs-*-*",
"beats-*"
],
"query": {
"size": 1,
"query": {
"bool": {
"must": [
{
"query_string": {
"query": "winlog.channel:Microsoft\\-Windows\\-Sysmon\\/Operational AND (event.code:1 AND (winlog.channel:Microsoft\\-Windows\\-Sysmon\\/Operational AND (event.code:1 AND (winlog.channel:Microsoft\\-Windows\\-Sysmon\\/Operational AND (event.code:1 AND ((process.executable:*\\\\whoami.exe OR process.pe.original_file_name:whoami.exe) AND process.command_line:*\\/priv*))))))",
"analyze_wildcard": true
}
}
]
}
}
}
}
}
],
"tags": [
"attack-privilege_escalation",
"attack-discovery",
"attack-t1033"
],
"triggers": [
{
"name": "generated-trigger",
"severity": 2,
"condition": {
"script": {
"source": "ctx.results[0].hits.total.value > 0",
"lang": "painless"
}
},
"actions": []
}
],
"sigma_meta_data": {
"rule_id": "97a80ec7-0e2f-4d05-9ef4-65760e634f6b",
"threat": []
},
"references": [
"https://docs.microsoft.com/en-us/windows-server/administration/windows-commands/whoami"
]
}
from sigma.backends.opensearch.opensearch_ppl import OpenSearchPPLBackend
from sigma.collection import SigmaCollection
# Instantiate PPL backend
backend = OpenSearchPPLBackend()
# Use the same rule as above
rules = SigmaCollection.from_yaml("""
title: Run Whoami Showing Privileges
id: 97a80ec7-0e2f-4d05-9ef4-65760e634f6b
status: experimental
description: Detects a whoami.exe executed with the /priv command line flag instructing the tool to show all current user privieleges. This is often used after a privilege escalation attempt.
references:
- https://docs.microsoft.com/en-us/windows-server/administration/windows-commands/whoami
author: Florian Roth
date: 2021/05/05
modified: 2022/05/13
tags:
- attack.privilege_escalation
- attack.discovery
- attack.t1033
logsource:
category: process_creation
product: windows
detection:
selection_img:
- Image|endswith: '\whoami.exe'
- OriginalFileName: 'whoami.exe'
selection_cli:
CommandLine|contains: '/priv'
condition: all of selection*
falsepositives:
- Administrative activity (rare lookups on current privileges)
level: high
""")
# Print converted rule in PPL syntax
print("PPL Result: \n" + "\n".join(backend.convert(rules)))
Resultado PPL:
source=windows-process_creation-* | where (LIKE(Image, "%\whoami.exe") OR OriginalFileName="whoami.exe") AND LIKE(CommandLine, "%/priv%")
from sigma.backends.opensearch.opensearch_ppl import OpenSearchPPLBackend
from sigma.collection import SigmaCollection
backend = OpenSearchPPLBackend()
# Brute force detection using event_count correlation
rules = SigmaCollection.from_yaml("""
title: Windows Failed Logon Event
name: failed_logon
logsource:
product: windows
service: security
detection:
selection:
EventID: 4625
filter:
SubjectUserName|endswith: $
condition: selection and not filter
---
title: Brute Force Attack Detection
correlation:
type: event_count
rules:
- failed_logon
group-by:
- TargetUserName
- TargetDomainName
timespan: 5m
condition:
gte: 10
""")
print("Correlation PPL Result: \n" + "\n".join(backend.convert(rules)))
Resultado da Correlação PPL:
| search source=windows-security-* | where EventID=4625 AND NOT LIKE(SubjectUserName, "%$") | stats count() as event_count by TargetUserName, TargetDomainName | where event_count >= 10
Você pode configurar o comportamento do backend PPL com atributos personalizados nas regras Sigma ou nas opções de inicialização do backend.
O backend PPL suporta os seguintes atributos personalizados que podem ser especificados na seção custom de uma regra Sigma:
custom:
opensearch_ppl_index: "custom-logs-*" # Override default index pattern
opensearch_ppl_min_time: "-30d" # Set query time window start
opensearch_ppl_max_time: "now" # Set query time window end
Este exemplo mostra como os atributos personalizados funcionam com regras de correlação, onde regras de detecção individuais podem ter suas próprias janelas de tempo ou herdar da regra de correlação:
title: Detection Rule 1 - With Own Time Filter
id: 10000400-0000-0000-0000-000000000004
logsource:
product: windows
category: process_creation
detection:
selection:
CommandLine|contains: 'malware'
condition: selection
custom:
opensearch_ppl_min_time: "-7d" # This rule uses 7 days
opensearch_ppl_max_time: "now"
---
title: Detection Rule 2 - No Time Filter
id: 10000401-0000-0000-0000-000000000004
logsource:
product: windows
category: network_connection
detection:
selection:
DestinationPort: 443
condition: selection
# No custom attributes - will inherit from correlation
---
title: Correlation - Mixed Time Filters
id: 10000402-0000-0000-0000-000000000004
correlation:
type: temporal
rules:
- 10000400-0000-0000-0000-000000000004
- 10000401-0000-0000-0000-000000000004
group-by:
- Computer
timespan: 5m
custom:
opensearch_ppl_min_time: "-30d" # Rule 2 inherits this (30 days)
opensearch_ppl_max_time: "now"
Resultado:
Você também pode definir valores padrão ao instanciar o backend:
backend = OpenSearchPPLBackend(
custom_logsource="default-logs-*", # Default index pattern for all rules
min_time="-24h", # Default time window start
max_time="now" # Default time window end
)
Atributos personalizados em regras individuais substituirão esses padrões de nível de backend.