ThreatHunting-Keywords

🎯 List of keywords for ThreatHunting sessions

What is Threat Hunting ?

Threat hunting is a proactive and iterative approach to detecting malicious activities within an organization's network or systems that may have bypassed automated security measures. Unlike reactive investigations triggered by security alerts, threat hunting is driven by threat intelligence (TI)-driven checks and hypotheses derived from systematic and opportunistic analysis. These hypotheses 💡 help hunters uncover unknown threats, potential threats, or known threats that may have evaded security detections, as well as vulnerabilities or indicators of compromise (IoCs) that automated systems might miss or exclude. The process also focuses on identifying precursors to alerts/dashboards and improving SOC/triage workflows while also contributing to shadow asset inventory management and escalates low/mid-fidelity events that require further investigation. The primary goal is to identify the tactics, techniques, and procedures (TTPs) used by threat actors, enhancing the organization’s ability to preemptively detect and mitigate potential attacks.

Advantages of Threat Hunting:

🔍 Identifiy Visibility Gaps
- Detects areas where monitoring and detection are insufficient, addressing blind spots in the network or systems to mitigate hidden threats.
🛠️ Fills Detection Gaps
- Proactively searching for anomalies, precursors, and TTPs that may not trigger alerts
⚙️ Improves SOC Efficiency
- Integrates threat hunting insights into SOC workflows to uncover events that static detections might miss. By reducing false positives and escalating overlooked events, it allows SOC analysts to focus on critical threats and refine detection logic for future use.
🚀 Enhances Threat Detection and supports Continuous Improvement
- Identifies advanced, unknown, or hidden threats that bypass automated detection systems and feeds valuable data back into SOC processes, improving tools, training, and future detection capabilities
🛡️ Reduces Attack Surface
- Discovers unmanaged or unauthorized systems, applications, and shadow IT within the network, enabling organizations to address security blind spots and reduce exposure to potential threats.

Bridging Threat Hunting With Core Services

Threat Hunting Lifecycle in SOC Operations

My process suggestion to organizing partially automated threat hunting sessions to maintain high-quality detection rules within a SOC

Detection Maturity Level

SOC teams focus on deploying high-fidelity detections across all levels of the Detection Maturity Pyramid, targeting known threats with minimal false positives. Threat hunting complements this by addressing unknown threats, advanced TTPs, and anomalies prone to high false-positive rates, bridging gaps and enhancing detection coverage beyond standard SOC capabilities.

Threat Hunting Checklist - Key Focus Areas for Intelligence Gathering

Targeted Threat Hunting Methodology Example

Ideally, each threat hunting session should have clear objectives. This flowchart provides a structured approach to guide your process, from preparation and investigation to actionable recommendations.

🎯 List of keywords for ThreatHunting sessions

Files

For the blueteam:

The ThreatHunting-Keywords Lists can be valuable for Threat Hunters, SOC and CERT teams for static analysis on SIEM as it assists in identifying threat actors (or redteamers 😆) using default configurations from renowned exploitation tools in logs. It differs from IOC feeds in its enduring relevance: the keywords here have no 'expiration dates' and can detect threats years after their inclusion, they are flexible accepting wildcard and non sensitive case matches and only focused on default keywords.

Primarily designed for Threat Hunting, this list can be useful in complex scenarios. Whether you have access to a SIEM you don't manage, with unparsed data, or if you're part of a SOC team with a well-managed SIEM, the examples provided here can expedite the process of detecting malicious activity without the necessity to parse anything. If your logs are already parsed, this list can be used to match fields within your data, potentially transforming into a detection rule based on the keyword type category you select, provided the false positive rate is sufficiently low.

⚠️ Not everything can be added in this list, we do not make complex behavior detections here, only simple keywords detections in fields or raw logs, aimed to detect default configurations

⚠️ A lot of tools in the list have dedicated detection rules, correlating events with thresholds and unique process relations... we will not cover all the possible detections for a tool here, only keywords detections

If you're part of a Security Operations Center (SOC) and are managing hundreds of detection rules that rely solely on simple keyword detections without any field or event correlation, consider rethinking your approach. In my opinion, these should not constitute individual detection rules. Instead, they might be better suited to a consolidated list like this one, although implementation might be more challenging if you're not using a platform like Splunk.

This approach encourages the creation of high-quality, purposeful rules while keeping your simple field keyword detections organized and manageable in one place. The end result? One comprehensive detection rule that covers all of them. This streamlines your process and optimizes your detection capabilities.

For Incident Responders, you can use this list during your investigation on raw logs or files to quickly identify known exploitation tools with the yara rules Yara Rules, a powershell script or by quickly ingesting your logs in splunk with Splunk4DFIR

For the redteam:

To evade detection by simple keyword detection, it is critical to recompile and rename all custom strings, class or function names, variable names, argument names, executable names, default user-agents, certificates, or any other strings that could potentially be associated with the tools you are using during your operation. Employ the most common names for everything to blend in with normal traffic. Scripts located here can assist you in identifying some of these.

However, if you're developing public "red team tools", consider aiding the blue team by using distinct names. Employ a default configuration with an exotic port, custom certificates, unique user-agents, specific function names and arguments that aren't common. This assists in creating a clear signature that can be used for simple keyword detections, so the blueteam can at least easily detect the script kiddies.

Content of the Threat Hunting Keywords File:

Header: keyword,metadata_keyword_type,metadata_tool,metadata_description,metadata_tool_techniques,metadata_tool_tactics,metadata_malwares_name,metadata_groups_name,metadata_category,metadata_link,metadata_enable_endpoint_detection,metadata_enable_proxy_detection,metadata_tags,metadata_comment,metadata_severity_score,metadata_popularity_score,metadata_github_stars,metadata_github_forks,metadata_github_created_at,metadata_github_updated_at
keyword: The entries in this column represent non-case-sensitive keywords used for Threat Hunting. These keywords are flexible, allowing the use of wildcards to broaden or narrow your search parameters as needed.
metadata_keyword_regex: The entries in this column represent the regex pattern detection for the keyword, these patterns are refined to offer precise detection capabilities, suitable for use with YARA, ripgrep or similar detection tools.
metadata_keyword_type: Type of the keywords, Currently, there are three types:
- 🛠️ offensive tool keyword: These keywords relate to offensive tools or exhibit high confidence of malicious intent. It's crucial that these terms hold relevance and reliability in detecting potential threats (low false positive rate)
- 🛠️ greyware tool keyword: Keywords in this category correspond to 'legitimate' tools that are abused by malicious actors. As these tools also have legitimate uses, the potential for false positives is inherently higher. It's important to interpret these results with the understanding that not all detections may signify malicious activity
- 🛠️ signature keyword: These keywords may not directly associate with tools but may include security product signature names, specific strings, or words significant in threat detection.
metadata_tool: Name of the tool we want to detect
metadata_description: description of the tool we want to detect
metadata_tool_techniques: MITRE techniques related to the tool we want to detect
metadata_tool_tactics: MITRE tactics related to the tool we want to detect
metadata_malwares_name: Names of malware variants that utilize the tool in question
metadata_groups_name: Names of Threat actors groups associated with the tool
metadata_category: Global category name of the tool. This may change later and suggestions are welcome.
metadata_link: link to the tool (source code, articles, samples, blog ...)
metadata_enable_endpoint_detection: Field indicating whether the keyword can be effectively employed in searches across endpoint logs. This includes but is not limited to Windows event logs, EDR, PowerShell logs, auditd, bastion sessions, Sysmon or any data source containing process and file activity fields.
- If you can search the keyword in endpoint logs, the value is 1 (enabled).
- If the keyword is not relevant for endpoint logs, the value is 0 (disabled).
metadata_enable_proxy_detection: Field indicating the applicability of the keyword for searches within network logs (Proxy, DNS logs or any data with queries and URLs originating from the internal network)
- If you can search the keyword in network activity logs, the value is 1 (enabled).
- If the keyword is not relevant for network activity logs, the value is 0 (disabled).
metadata_popularity_score: score from 1 to 10 (low to high popularity)
metadata_severity_score: score from 1 to 10 (low to high severity)
metadata_tags: tags to identify specific artifacts, multiple tags can be associated with a keyword, when some of the specific artifacts can't be added in the lists without more detection context, they are added in my other awesome lists for_detection
metadata_comment: This field may contain a useful comment added for the keyword.
metadata_github_stars: Number of stars on the github project (if the tool is on github, if elsewhere the value is N/A) this is used to calculate the popularity score
metadata_github_forks: Number of forks on the github project (if the tool is on github, if elsewhere the value is N/A) can be used for dashboard stats of the most used tools
metadata_github_created_at: Creation date of the github project (if the tool is on github, if elsewhere the value is N/A) can be used for dashboard stats
metadata_github_updated_at: Last updated date of the github project (if the tool is on github, if elsewhere the value is N/A) can be used to track important offensive tools updates and adjust keywords detections

Use the List to hunt with Splunk:

upload the list threathunting-keywords.csv on Splunk
create a lookup definition named threathunting-keywords for the lookup threathunting-keywords.csv
- in the advanced options, add the Match type WILDCARD(keyword) and make sure Case sensitive match is not checked

transforms.conf

[threathunting-keywords]
batch_index_query = 0
case_sensitive_match = 0
filename = threathunting-keywords.csv
match_type = WILDCARD(keyword)

now we can use our lookup definition to hunt 🏹
⚠️ if the following section searches don't appear to be working it may be due to splunk resource limitation settings, especially if you are running splunk under default configuration. For starter you may have to consider increasing the [lookup] stanza max_memtable_bytes value.

Example use cases with `threathunting-keywords`:

Hunt all the keywords in raw logs 😱

`myendpointslogs` 
| lookup threathunting-keywords keyword as _raw OUTPUT keyword as keyword_detection metadata_keyword_type metadata_tool metadata_description metadata_tool_techniques metadata_tool_tactics metadata_malwares_name metadata_groups_name metadata_category	metadata_link	metadata_enable_endpoint_detection metadata_enable_proxy_detection metadata_comment
| search metadata_description!="" AND metadata_enable_endpoint_detection=1
| stats count earliest(_time) as firsttime latest(_time) as lasttime values(_raw) as raw by metadata_keyword_type keyword_detection index sourcetype 
| convert ctime(*time)

Send the job to background and keep the job ID.

myendpointslogs is a macro that will search for all your endpoints logs, could be windows logs, EDR telemetry, sysmon, auditd, bastion sessions, powershell execution logs or any other logs that will monitor for process activity or file activity. (if you don't use macro, you can replace the macro with your index, tag or datamodel)
- I intentionally incorporated a macro in the examples to demonstrate that the initial exploration can be done with any method that suits one's needs, be it a data model, tstats, tags, index/sourcetype. While tstats coupled with a datamodel is often the fastest route, filter with the command TERM() for specific searches before the |lookup if needed
| lookup this is very important, for big lookups like this one, always use |lookup instead of |inputlookup, |lookup will use the lookup pushed on the indexers when the bundle is replicated but |inputlookup will send to the indexers the search with all the lookup content each time, using |lookup we gain massive performances here (this search will execute for hours on big environments so better optimize our search.
... keyword as _raw OUTPUT keyword as keyword_detection this is the part where we will match the field named _raw with the field keyword in our lookup , in splunk _raw is the raw log without any parsing (our use case). When a keyword match, the field keyword_detection will show the keyword in the lookup that matched on the field (_raw) here.
| search metadata_description!="" AND metadata_enable_endpoint_detection=1 we only focus on endpoints logs here so we add metadata_enable_endpoint_detection=1 to only match on the relevant keywords for endpoint logs and metadata_description!="" to only have the matched keywords
| stats count earliest(_time) as firsttime latest(_time) as lasttime values(_raw) as raw by metadata_keyword_type keyword_detection index sourcetype here i made a quick filter without all the fields of the lookup for the example (but you can also add them if you want to have more exclusions possibilities), this allows us to easily have an idea of which keyword is matching a lot so we can easily exclude the category or the tool if we have too many false positives for it !
When my search is finished, how can i analyze the results ? you will have the raw logs ordered by keywords and keyword types, using |loadjob myjobid we can now manipulate the output with the relevant logs without searching again on all the logs.

Filter the result

| loadjob 1684146257.1495958
| search
  NOT (keyword_detection IN ("fixme","fixme","fixme")) 
  NOT (metadata_keyword_type IN ("fixme","fixme"))
  NOT (raw IN ("fixme","fixme","fixme"))

Exclude the required keywords, raw text or keyword types. if if decide to exclude greyware tool keyword type (legitimate tools keywords that are abused by attackers) because this environment has too many results for this kind of tools, we have two options:

Filter at the beggining of our initial search

`myendpointslogs` 
| lookup threathunting-keywords keyword as _raw OUTPUT keyword as keyword_detection metadata_keyword_type metadata_tool metadata_description metadata_tool_techniques metadata_tool_tactics metadata_malwares_name metadata_groups_name metadata_category	metadata_link	metadata_enable_endpoint_detection metadata_enable_proxy_detection metadata_comment
| search metadata_description!="" metadata_keyword_type="offensive tool keyword" metadata_enable_endpoint_detection=1
| stats count earliest(_time) as firsttime latest(_time) as lasttime values(_raw) as raw by metadata_keyword_type keyword_detection index sourcetype 
| convert ctime(*time)

I added a metadata_keyword_type="offensive tool keyword" to only focus on offensive tools that i am sure are used by malicious actors

Or filter after our initial search (to gain a lot of time):

| loadjob 1684146257.1495958
| search metadata_keyword_type="offensive tool keyword"

So that was our Use case to search on raw logs in endpoint logs, if we want to search the keywords for network logs (anything that can log a query or an url), we simply change it to:

`mynetworklogs` 
| lookup threathunting-keywords keyword as _raw OUTPUT keyword as keyword_detection metadata_keyword_type metadata_tool metadata_description metadata_tool_techniques metadata_tool_tactics metadata_malwares_name metadata_groups_name metadata_category	metadata_link	metadata_enable_endpoint_detection metadata_enable_proxy_detection metadata_comment
| search metadata_description!="" AND metadata_enable_proxy_detection=1
| stats count earliest(_time) as firsttime latest(_time) as lasttime values(_raw) as raw by metadata_keyword_type keyword_detection index sourcetype 
| convert ctime(*time)

Now it is the same as the first search but i changed the datasource for mynetworklogs and added metadata_enable_proxy_detection=1 to match the relevant keywords for networklogs (better have proxy and DNS logs for this)

Hunt the keywords in other fields 🙂 (url,process,commandline,query...):

Match only on url field:

`mynetworklogs` url=*
| lookup threathunting-keywords keyword as url OUTPUT keyword as keyword_detection metadata_keyword_type metadata_tool metadata_description metadata_tool_techniques metadata_tool_tactics metadata_malwares_name metadata_groups_name metadata_category	metadata_link	metadata_enable_endpoint_detection metadata_enable_proxy_detection metadata_comment
| search metadata_description!="" AND metadata_enable_proxy_detection=1
| stats count earliest(_time) as firsttime latest(_time) as lasttime values(url) as url by src_ip metadata_keyword_type keyword_detection index sourcetype 
| convert ctime(*time)

Match only on query field:

`mynetworklogs` query=*
| lookup threathunting-keywords keyword as query OUTPUT keyword as keyword_detection metadata_keyword_type metadata_tool metadata_description metadata_tool_techniques metadata_tool_tactics metadata_malwares_name metadata_groups_name metadata_category	metadata_link	metadata_enable_endpoint_detection metadata_enable_proxy_detection metadata_comment
| search metadata_description!="" AND metadata_enable_proxy_detection=1
| stats count earliest(_time) as firsttime latest(_time) as lasttime values(query) as query by src_ip metadata_keyword_type keyword_detection index sourcetype 
| convert ctime(*time)

Match on multiple fields at the same time, example for endpoint logs:

`myendpointslogs` 
| eval myfields=mvappend(service, process, process_command, parent_process, parent_process_command, grand_parent_process, grand_parent_process_command, file_path, file_name)
| lookup threathunting-keywords keyword as myfields OUTPUT keyword as keyword_detection metadata_keyword_type metadata_tool metadata_description metadata_tool_techniques metadata_tool_tactics metadata_malwares_name metadata_groups_name metadata_category	metadata_link	metadata_enable_endpoint_detection metadata_enable_proxy_detection metadata_comment
| search metadata_description!="" AND metadata_enable_endpoint_detection=1
| stats count earliest(_time) as firsttime latest(_time) as lasttime values(process) values(service) values(process_command) values(file_name) values(file_path) values(parent_process) values(parent_process_command) values(grand_parent_process) values(grand_parent_process_command) by metadata_keyword_type keyword_detection index sourcetype 
| convert ctime(*time)

Speed:

If the speed is a concern or you're planning to implement this as a scheduled detection rule, you might want to consider splitting the lookup into diffent lookups by choosing the metadata_keyword_type or metadata_tool column you want to use.

Note that filtering using the search command after the |lookup doesn't expedite the search process. If you want to concentrate on a specific portion of the lookup without dividing it, you should use the |inputlookup command along with the where clause. While this method may consume more CPU resources, it generally results in faster execution. For more details, check out the Splunk documentation on inputlookup: https://docs.splunk.com/Documentation/Splunk/latest/SearchReference/Inputlookup

With ELK:

If you are working with the Elastic Stack, there is a lot of restrictions for lists (you cannot use special caracters, spaces ...), you have 3 options:

Use another list available here in the same repo https://github.com/mthcht/ThreatHunting-Keywords/tree/main/elk (it's not a direct extract of threathunting-keywords.csv, it's modified for ELK and not updated)
Use Sigma "hunting" rules, directly extracted from this project https://github.com/mthcht/ThreatHunting-Keywords-sigma-rules with pysigma for the convertion
Use some of my lists as an IOC list with wildcard queries https://www.elastic.co/guide/en/elasticsearch/reference/8.15/query-dsl-wildcard-query.html#wildcard-top-level-params

Dashboard Example

Splunk4DFIR

Another example of using the project csv files with splunk to hunt in DFIR artifacts and logs: https://github.com/mf1d3l/Splunk4DFIR

Other awesome lists for detection

I keep some relevant artefacts in separated lists, these lists are more precise and can be used in detection rules, they are available in this github repo you will find:

My intelligence-gathering sheet for planning Threat Hunting sessions

📋 Lists: https://github.com/mthcht/awesome-lists/tree/main/Lists
🕵️‍♂️ ThreatHunting Guides: https://mthcht.medium.com/list/threat-hunting-708624e9266f
🚰 Suspicious Named pipes: suspicious_named_pipe_list.csv
🌐 Suspicious TLDs (updated automatically): [suspicious_TLDs]
🌐 Suspicious ASNs (updated automatically): [suspicious ASNs]
🔧 Suspicious Windows Services: suspicious_windows_services_names_list.csv
⏲️ Suspicious Windows Tasks: suspicious_windows_tasks_list.csv
🚪 Suspicious destination port: suspicious_ports_list.csv
🛡️ Suspicious Firewall rules: suspicious_windows_firewall_rules_list.csv
🆔 Suspicious User-agent: suspicious_http_user_agents_list.csv
📇 Suspicious USB Ids: suspicious_usb_ids_list.csv
🔢 Suspicious MAC address: suspicious_mac_address_list.csv
📛 Suspicious Hostname: suspicious_hostnames_list.csv
🧮 Metadata Executables: executables_metadata_informations_list.csv
🕸️ DNS over HTTPS server list: dns_over_https_servers_list.csv
📚 Hijacklibs (updated automatically): hijacklibs_list.csv
🌐 TOR Nodes Lists (updated automatically): https://github.com/mthcht/awesome-lists/tree/main/Lists/TOR
🛠️ LOLDriver List (updated automatically): loldrivers_only_hashes_list.csv
🛠️ Malicious Bootloader List (updated automatically): malicious_bootloaders_only_hashes_list.csv
📜 Malicious SSL Certificates List (updated automatically): ssl_certificates_malicious_list.csv
🖥️ RMM detection: https://github.com/mthcht/awesome-lists/tree/main/Lists/RMM
👤🔑 Important Roles and groups for AD/EntraID/AWS: [permissions]
💻🔒 Ransomware known file extensions: ransomware_extensions_list.csv
💻🔒 Ransomware known file name ransom notes: ransomware_notes_list.csv
📝 Windows ASR rules: windows_asr_rules.csv
🌐 DNSTWIST Lists (updated automatically): DNSTWIST Default Domains + script
🌍 VPN IP address Lists (updated automatically):
- 🛡️ NordVPN: nordvpn_ips_list.csv
- 🛡️ ProtonVPN: protonvpn_ip_list.csv
🏢 Companies IP Range Lists (updated automatically): Default Lists + script
🔗 Others correlation Lists: https://github.com/mthcht/awesome-lists/tree/main/Lists/Others
📋 Lists i need to finish: https://github.com/mthcht/awesome-lists/tree/main/todo

Check out these Guides to use some of the lists:

... more here

DFIR Hunt for keywords in files (No SIEM)

After conducting a thorough review of various tools, I discovered that ripgrep significantly outperforms its competitors when it comes to rapidly matching an extensive list of regex patterns against each line of a large log file or even multiple files simultaneously. It proved to be the most efficient solution for handling massive amounts of data, providing unparalleled speed and flexibility.

Hunt for evil in log file(s) with Ripgrep and the 'only_keywords_regex.txt' list

`rg.exe -f .\only_keywords_regex.txt .\EvtxECmd_Output.csv --multiline --ignore-case`

.\only_keywords_regex.txt serves as the source file for the threat hunting keywords, transformed into regex patterns for precise matching. These patterns originate from the threathunting-keywords.csv file, which has undergone a conversion process for optimal compatibility with regex operations.
.\EvtxECmd_Output.csv represents the target file in which the search will be conducted. In this context, it is a .csv format of a Windows event log, produced by exporting evtx logs. However, the flexibility of ripgrep allows you to replace this with any file of your choosing for detailed pattern search operations.
--multiline option enables ripgrep to effectively handle and match patterns spanning across multiple lines, significantly broadening the scope of the search. You will get the matched lines like this with the line number (but without the matching keyword)

Better option for very large files (on windows):

DFIR_hunt_in_file.ps1

powershell -ep Bypass -File .\DFIR_hunt_in_file.ps1 -patternFile "only_keywords_regex.txt" -targetFile "C:\Users\mthcht\collection\20230406154410_EvtxECmd_Output.csv" -rgPath "C:\Users\mthcht\Downloads\ripgrep-13.0.0-x86_64-pc-windows-msvc\ripgrep-13.0.0-x86_64-pc-windows-msvc\rg.exe"

-targetFile: specify the file to search in (in the example, a DFIR-ORC extract logs)
-patternFile: the file containing the regex patterns only_keywords_regex.txt
-rgPath: the path of the ripgrep executable

content of the powershell script (included in the repo):

param (
    [Parameter(Mandatory=$true)]
    [string]$patternFile,
    [Parameter(Mandatory=$true)]
    [string]$targetFile,
    [Parameter(Mandatory=$true)]
    [string]$rgPath
)

Start-Transcript -Path "$PSScriptRoot\result_search.log" -Append -Force -Verbose

$totalLines = (Get-Content $patternFile | Measure-Object -Line).Lines
$currentLine = 0
Get-Content $patternFile | ForEach-Object {
    $currentLine++
    Write-Host "Searching for pattern $currentLine of $totalLines : $_"  
    & $rgPath --multiline --ignore-case $_ $targetFile | Write-Output 
}

Stop-Transcript -Verbose

The result of the search will be in result_search.log in the same directory as the script.

Better option for verylarge files (on linux):

todo

Hunt for evil in file only with powershell and the 'only_keywords.txt ' list (slower not recommmanded)

In powershell it's much slower but if you still want to do it this way, you can use the script below, it will tell you the line number matched and the corresponding keyword:

powershell.exe -ep Bypass -File .\hunt_keywords_windows.ps1 -k .\only_keywords.txt -f .\EvtxECmd_Output.csv

param(
    [Parameter(Mandatory=$true)]
    [string]$file,
    
    [Parameter(Mandatory=$true)]
    [string]$kw
)

$Keywords = Get-Content $kw
$result = @()

foreach ($Keyword in $Keywords) {
    $SearchTerm = $Keyword.Replace("*", ".*")
    $SearchTerm = [Regex]::Escape($SearchTerm).Replace("\.\*", ".*")
    
    $reader = New-Object System.IO.StreamReader($file)
    $lineNumber = 0
    while (($line = $reader.ReadLine()) -ne $null) {
        $lineNumber++
        if ($line -match $SearchTerm) {
            $result += New-Object PSObject -Property @{
                'Keyword' = $Keyword
                'LineNumber' = $lineNumber
                'Line' = $line
            }
        }
    }
    $reader.Close()
}

$result | Out-GridView
Read-Host -Prompt "Press Enter to exit"

YARA Rules

All the detection patterns of this project are automatically exported to yara rules in ThreatHunting-Keywords-yara-rules

Some hunting example with the yara rules:

Quick datatable to search for keyword

https://mthcht.github.io/ThreatHunting-Keywords/

False positives

Contribute and add your false positives to the expected false positives list

SIGMA Rules

Check out the lookup translated in SIGMA rules i usually update it at the same time :)

MITRE ATT&CK technique mapping

with splunk addon https://splunkbase.splunk.com/app/5742

Coverage for 2242 tools (updated the 2024/08/30):

splunk search:

| inputlookup threathunting-keywords.csv
| stats count by metadata_tool metadata_tool_techniques
| makemv delim=" - " metadata_tool_techniques
| mvexpand metadata_tool_techniques
| stats count by metadata_tool_techniques

and use this splunk visualization: https://splunkbase.splunk.com/app/5742

Tools matrix

Splunk dashboards (this is just one example; a wide variety of filters can be applied using the available fields in the file):

Threat Actor Groups by tools in this project

splunk xml dashboard example:

<form version="1.1">
  <label>tools matrix</label>
  <description>tools_matrix</description>
  <fieldset submitButton="false" autoRun="true">
    <input type="multiselect" token="category" searchWhenChanged="true">
      <label>tool categories</label>
      <fieldForLabel>metadata_category</fieldForLabel>
      <fieldForValue>metadata_category</fieldForValue>
      <search>
        <query>| inputlookup threathunting-keywords.csv
| stats count by metadata_category
|  fields - count</query>
        <earliest>-24h@h</earliest>
        <latest>now</latest>
      </search>
      <choice value="*">all</choice>
      <prefix>metadata_category IN (</prefix>
      <suffix>)</suffix>
      <initialValue>*</initialValue>
      <valuePrefix>"</valuePrefix>
      <valueSuffix>"</valueSuffix>
      <delimiter> ,</delimiter>
    </input>
    <input type="multiselect" token="groups" searchWhenChanged="true">
      <label>groups name</label>
      <choice value="*">ALL</choice>
      <prefix>metadata_groups_name IN (</prefix>
      <suffix>)</suffix>
      <initialValue>*</initialValue>
      <valuePrefix>"</valuePrefix>
      <valueSuffix>"</valueSuffix>
      <delimiter>,</delimiter>
      <fieldForLabel>metadata_groups_name</fieldForLabel>
      <fieldForValue>metadata_groups_name</fieldForValue>
      <search>
        <query>| inputlookup threathunting-keywords.csv
| stats count by metadata_groups_name
|  fields - count
| eval metadata_groups_name = split(metadata_groups_name, " - ")
| mvexpand metadata_groups_name
|  dedup metadata_groups_name</query>
        <earliest>-24h@h</earliest>
        <latest>now</latest>
      </search>
    </input>
  </fieldset>
  <row>
    <panel>
      <viz type="sankey_diagram_app.sankey_diagram">
        <search>
          <query>| inputlookup threathunting-keywords.csv
| search metadata_groups_name!=N/A $category$
| stats count as detection_patterns by metadata_groups_name metadata_tool
| eval metadata_groups_name = split(metadata_groups_name, " - ")
| mvexpand metadata_groups_name
| search $groups$</query>
          <earliest>-24h@h</earliest>
          <latest>now</latest>
        </search>
        <option name="drilldown">none</option>
        <option name="height">728</option>
        <option name="refresh.display">progressbar</option>
        <option name="sankey_diagram_app.sankey_diagram.colorMode">categorical</option>
        <option name="sankey_diagram_app.sankey_diagram.maxColor">#3fc77a</option>
        <option name="sankey_diagram_app.sankey_diagram.minColor">#d93f3c</option>
        <option name="sankey_diagram_app.sankey_diagram.numOfBins">6</option>
        <option name="sankey_diagram_app.sankey_diagram.showBackwards">false</option>
        <option name="sankey_diagram_app.sankey_diagram.showLabels">true</option>
        <option name="sankey_diagram_app.sankey_diagram.showLegend">true</option>
        <option name="sankey_diagram_app.sankey_diagram.showSelf">false</option>
        <option name="sankey_diagram_app.sankey_diagram.showTooltip">true</option>
        <option name="sankey_diagram_app.sankey_diagram.styleBackwards">false</option>
        <option name="sankey_diagram_app.sankey_diagram.useColors">true</option>
      </viz>
    </panel>
  </row>
</form>

🤝 Contributing

Contributions, issues and feature requests are welcome!

If you want me to add a tool to the list, create a issue with this template:

Tool Name:

``

Please provide the name of the tool.

Official Website or Source Code Link:

``

Provide a link to the tool's official website or source code repository (GitHub, GitLab, etc.). If documentation is available, please include it.

Tool Description:

``

Describe the tool's purpose, functionality, and notable features. If you're unsure, leave this blank, and I'll review the tool in more detail.

Known Usage by Malicious Actors (if applicable):

``

If you have information on known or potential misuse of this tool by malicious actors, please share it here.

Tool Classification:

Please choose the most appropriate category for the tool:

Offensive (Primarily used by attackers, not legitimate for general use)
Greyware (Legitimate tool that is often abused by malicious actors)
Generic Signatures/Keywords (Common signatures or terms linked to malware as detected by security products)

Propose changes to the list with a PR (provide false positives feedbacks, logs sample if you can), if a keyword is generating too many false positives in too many environments we can delete it)

I will decide whether a tool is worth adding to the list. Tools that are widely used and recognized in the community are more likely to be included than obscure or new ones

Name		Name	Last commit message	Last commit date
Latest commit History 526 Commits
.github		.github
_false_positives		_false_positives
_utils		_utils
elk		elk
images		images
old		old
release_notes		release_notes
tools		tools
.gitignore		.gitignore
DFIR_hunt_in_file.ps1		DFIR_hunt_in_file.ps1
README.md		README.md
greyware_tool_keyword.csv		greyware_tool_keyword.csv
index.html		index.html
offensive_tool_keyword.csv		offensive_tool_keyword.csv
offensive_tool_keyword_network_detection.csv		offensive_tool_keyword_network_detection.csv
only_keywords.txt		only_keywords.txt
only_keywords_regex.txt		only_keywords_regex.txt
only_keywords_regex_better_perf.txt		only_keywords_regex_better_perf.txt
signature_keyword.csv		signature_keyword.csv
threathunting-keywords.csv		threathunting-keywords.csv

mthcht/ThreatHunting-Keywords

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