- title: Malicious PowerShell Keywords
id: f62176f3-8128-4faa-bf6c-83261322e5eb
status: experimental
description: Detects keywords from well-known PowerShell exploitation frameworks
modified: 2019/01/22
references:
- https://adsecurity.org/?p=2921
tags:
- attack.execution
- attack.t1086
author: Sean Metcalf (source), Florian Roth (rule)
logsource:
product: windows
service: powershell
definition: It is recommended to use the new "Script Block Logging" of PowerShell
v5 https://adsecurity.org/?p=2277
category: null
detection:
keywords:
Message:
- '*AdjustTokenPrivileges*'
- '*IMAGE_NT_OPTIONAL_HDR64_MAGIC*'
- '*Microsoft.Win32.UnsafeNativeMethods*'
- '*ReadProcessMemory.Invoke*'
- '*SE_PRIVILEGE_ENABLED*'
- '*LSA_UNICODE_STRING*'
- '*MiniDumpWriteDump*'
- '*PAGE_EXECUTE_READ*'
- '*SECURITY_DELEGATION*'
- '*TOKEN_ADJUST_PRIVILEGES*'
- '*TOKEN_ALL_ACCESS*'
- '*TOKEN_ASSIGN_PRIMARY*'
- '*TOKEN_DUPLICATE*'
- '*TOKEN_ELEVATION*'
- '*TOKEN_IMPERSONATE*'
- '*TOKEN_INFORMATION_CLASS*'
- '*TOKEN_PRIVILEGES*'
- '*TOKEN_QUERY*'
- '*Metasploit*'
- '*Mimikatz*'
condition: keywords
falsepositives:
- Penetration tests
level: high
In [ ]:
from elasticsearch import Elasticsearch
from elasticsearch_dsl import Search
import pandas as pd
In [ ]:
es = Elasticsearch(['http://helk-elasticsearch:9200'])
searchContext = Search(using=es, index='logs-endpoint-winevent-powershell-*', doc_type='doc')
In [ ]:
s = searchContext.query('query_string', query='Message.keyword:(*AdjustTokenPrivileges* OR *IMAGE_NT_OPTIONAL_HDR64_MAGIC* OR *Microsoft.Win32.UnsafeNativeMethods* OR *ReadProcessMemory.Invoke* OR *SE_PRIVILEGE_ENABLED* OR *LSA_UNICODE_STRING* OR *MiniDumpWriteDump* OR *PAGE_EXECUTE_READ* OR *SECURITY_DELEGATION* OR *TOKEN_ADJUST_PRIVILEGES* OR *TOKEN_ALL_ACCESS* OR *TOKEN_ASSIGN_PRIMARY* OR *TOKEN_DUPLICATE* OR *TOKEN_ELEVATION* OR *TOKEN_IMPERSONATE* OR *TOKEN_INFORMATION_CLASS* OR *TOKEN_PRIVILEGES* OR *TOKEN_QUERY* OR *Metasploit* OR *Mimikatz*)')
response = s.execute()
if response.success():
df = pd.DataFrame((d.to_dict() for d in s.scan()))
In [ ]:
df.head()