We recently released a new version of MSTICPy with a feature called Pivot functions. You must have msticpy installed to run this notebook:
%pip install --upgrade msticpy
MSTICpy versions >= 1.0.0
This feature has three main goals:
Here are a couple of examples showing calling different kinds of enrichment functions from the IpAddress entity:
>>> from msticpy.datamodel.entities import IpAddress, Host
>>> IpAddress.util.ip_type(ip_str="157.53.1.1"))
ip result
157.53.1.1 Public
>>> IpAddress.util.whois("157.53.1.1"))
asn asn_cidr asn_country_code asn_date asn_description asn_registry nets .....
NA NA US 2015-04-01 NA arin [{'cidr': '157.53.0.0/16'...
>>> IpAddress.util.geoloc(value="157.53.1.1"))
CountryCode CountryName State City Longitude Latitude Asn...
US United States None None -97.822 37.751 None...
This second example shows a pivot function that does a data query for host logon events from a Host entity.
>>> Host.AzureSentinel.list_host_logons(host_name="VictimPc")
Account EventID TimeGenerated Computer SubjectUserName SubjectDomainName
NT AUTHORITY\SYSTEM 4624 2020-10-01 22:39:36.987000+00:00 VictimPc.Contoso.Azure VictimPc$ CONTOSO
NT AUTHORITY\SYSTEM 4624 2020-10-01 22:39:37.220000+00:00 VictimPc.Contoso.Azure VictimPc$ CONTOSO
NT AUTHORITY\SYSTEM 4624 2020-10-01 22:39:42.603000+00:00 VictimPc.Contoso.Azure VictimPc$ CONTOSO
The pivot functionality exposes operations relevant to a particular entity as methods (or functions) of that entity. These operations include:
You can also add other functions from 3rd party Python packages or ones you write yourself as pivot functions.
Before we get into things let's clear up a few terms.
These are Python classes that represent real-world objects commonly encountered in CyberSec investigations and hunting. E.g. Host, URL, IP Address, Account, etc.
This comes from the common practice in CyberSec investigations of navigating from one suspect entity to another. E.g. you might start with an alert identifying a potentially malicious IP Address, from there you 'pivot' to see which hosts or accounts were communicating with that address. From there you might pivot again to look at processes running on the host or Office activity for the account.
This article is available in Notebook form so that you can try out the examples. [TODO]
There is also full documenation of the Pivot functionality on our ReadtheDocs page
Before Pivot functions your ability to use the various bits of functionality in MSTICPy was always bounded by you knowledge of where a certain function was (or your enthusiasm for reading the docs).
For example, suppose you had an IP address that you wanted to do some simple enrichment on.
ip_addr = "20.72.193.242"
First you'd need to locate and import the functions. There might also be (as in the GeoIPLiteLookup class) some initialization step you'd need to do before using the functionality.
from msticpy.context.ip_utils import get_ip_type
from msticpy.context.ip_utils import get_whois_info
from msticpy.context.geoip import GeoLiteLookup
geoip = GeoLiteLookup()
Next you might have to check the help for each function to work it parameters.
help(get_ip_type)
Help on function get_ip_type in module msticpy.context.ip_utils: get_ip_type(ip: str = None, ip_str: str = None) -> str Validate value is an IP address and determine IPType category. (IPAddress category is e.g. Private/Public/Multicast). Parameters ---------- ip : str The string of the IP Address ip_str : str The string of the IP Address - alias for `ip` Returns ------- str Returns ip type string using ip address module
Then finally run the functions
get_ip_type(ip_addr)
'Public'
get_whois_info(ip_addr)
('MICROSOFT-CORP-MSN-AS-BLOCK, US', {'nir': None, 'asn_registry': 'arin', 'asn': '8075', 'asn_cidr': '20.64.0.0/10', 'asn_country_code': 'US', 'asn_date': '2017-10-18', 'asn_description': 'MICROSOFT-CORP-MSN-AS-BLOCK, US', 'query': '20.72.193.242', 'nets': [{'cidr': '20.34.0.0/15, 20.48.0.0/12, 20.36.0.0/14, 20.40.0.0/13, 20.33.0.0/16, 20.128.0.0/16, 20.64.0.0/10', 'name': 'MSFT', 'handle': 'NET-20-33-0-0-1', 'range': '20.33.0.0 - 20.128.255.255', 'description': 'Microsoft Corporation', 'country': 'US', 'state': 'WA', 'city': 'Redmond', 'address': 'One Microsoft Way', 'postal_code': '98052', 'emails': ['msndcc@microsoft.com', 'IOC@microsoft.com', 'abuse@microsoft.com'], 'created': '2017-10-18', 'updated': '2017-10-18'}], 'raw': None, 'referral': None, 'raw_referral': None})
geoip.lookup_ip(ip_addr)
([{'continent': {'code': 'NA', 'geoname_id': 6255149, 'names': {'de': 'Nordamerika', 'en': 'North America', 'es': 'Norteamérica', 'fr': 'Amérique du Nord', 'ja': '北アメリカ', 'pt-BR': 'América do Norte', 'ru': 'Северная Америка', 'zh-CN': '北美洲'}}, 'country': {'geoname_id': 6252001, 'iso_code': 'US', 'names': {'de': 'USA', 'en': 'United States', 'es': 'Estados Unidos', 'fr': 'États-Unis', 'ja': 'アメリカ合衆国', 'pt-BR': 'Estados Unidos', 'ru': 'США', 'zh-CN': '美国'}}, 'location': {'accuracy_radius': 1000, 'latitude': 47.6032, 'longitude': -122.3412, 'time_zone': 'America/Los_Angeles'}, 'registered_country': {'geoname_id': 6252001, 'iso_code': 'US', 'names': {'de': 'USA', 'en': 'United States', 'es': 'Estados Unidos', 'fr': 'États-Unis', 'ja': 'アメリカ合衆国', 'pt-BR': 'Estados Unidos', 'ru': 'США', 'zh-CN': '美国'}}, 'subdivisions': [{'geoname_id': 5815135, 'iso_code': 'WA', 'names': {'en': 'Washington', 'es': 'Washington', 'fr': 'Washington', 'ja': 'ワシントン州', 'ru': 'Вашингтон', 'zh-CN': '华盛顿州'}}], 'traits': {'ip_address': '20.72.193.242', 'prefix_len': 18}}], [IpAddress(Address=20.72.193.242, Location={ 'AdditionalData': {}, 'CountryCode': 'US', ...)])
At which point you'd discover that the output from each function was somewhat raw and it would take a bit more work if you wanted to combine it in any way (say in a single table).
We'll see how pivot functions address these problems in the remainder of the notebook.
Typically we use MSTICPy's init_notebook
function that handles
checking versions and importing some commonly-used packages and modules
(both MSTICPy and 3rd party packages like pandas
import msticpy as mp
mp.init_notebook(verbosity=0);
The Pivot subsystem is loaded as part of the init_notebook
process. This also import entities such as IpAddress, Host, Url, etc.
into the notebook namespace.
One class of pivot functions that are not added to entities
in init_notebook
is data queries. These are loaded when you
create and connect to a QueryProvider
Let's load our data query provider for MS Sentinel
qry_prov = QueryProvider("MSSentinel")
msticpy.init_notebook loads and instantiates the Pivot class.
You can do that manually, if needed:
from msticpy.init.pivot import Pivot
pivot = Pivot(namespace=globals())
Why do we need to pass namespace=globals()
?
Pivot searches through the current objects defined in the Python/notebook
namespace. This is most relevant for QueryProviders. In most other cases
(like GeoIP and ThreatIntel providers, it will create new ones if it
can't find existing ones).
The simplest way to do this is to use the entities.find_entity
function.
entities.find_entity("ip")
Match found 'IpAddress'
msticpy.datamodel.entities.ip_address.IpAddress
entities.find_entity("azure")
No exact match found for 'azure'. Closest matches are 'AzureResource', 'Url', 'Malware'
Once you have the entity you can use the pivots()
function to see which pivot functions are available for it.
IpAddress.pivots()
['RiskIQ.articles', 'RiskIQ.artifacts', 'RiskIQ.certificates', 'RiskIQ.components', 'RiskIQ.cookies', 'RiskIQ.hostpair_children', 'RiskIQ.hostpair_parents', 'RiskIQ.malware', 'RiskIQ.projects', 'RiskIQ.reputation', 'RiskIQ.resolutions', 'RiskIQ.services', 'RiskIQ.summary', 'RiskIQ.trackers', 'RiskIQ.whois', 'VT.vt_communicating_files', 'VT.vt_historical_ssl_certificates', 'VT.vt_historical_whois', 'VT.vt_referrer_files', 'VT.vt_resolutions', 'VT.vt_subdomains', 'geoloc', 'ip_type', 'ti.lookup_ip', 'tilookup_ip', 'util.geoloc', 'util.geoloc_ips', 'util.ip_rev_resolve', 'util.ip_type', 'util.whois', 'vt_communicating_files', 'vt_historical_ssl_certificates', 'vt_historical_whois', 'vt_referrer_files', 'vt_resolutions', 'vt_subdomains', 'whois']
Some of the function names are a little unweildy but, in many cases, this is necessary to avoid name collisions. You might notice from the list that the functions are grouped into containers such as "ti" and "util" in the above example.
Although this makes the function name even longer we thought that this helped to keep related functionality together - so you don't get a TI lookup, when you thought you were running a query.
Fortunately Jupyter notebooks/IPython support tab completion so you should not normally have to remember these names.
The containers ("util", etc.) are also callable functions - they just return the list of functions they contain.
IpAddress.util()
geoloc (pivot function) geoloc_ips (pivot function) ip_rev_resolve (pivot function) ip_type (pivot function) whois (pivot function)
Now we're ready to run any of the functions for this entity
ip_addr = "20.72.193.242"
IpAddress.util.ip_type(ip_addr)
ip | result | src_row_index | |
---|---|---|---|
0 | 20.72.193.242 | Public | 0 |
IpAddress.util.whois(ip_addr)
asn | asn_cidr | asn_country_code | asn_date | asn_description | asn_registry | nets | nir | query | raw | raw_referral | referral | |
---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 8075 | 20.64.0.0/10 | US | 2017-10-18 | MICROSOFT-CORP-MSN-AS-BLOCK, US | arin | [{'cidr': '20.40.0.0/13, 20.34.0.0/15, 20.48.0.0/12, 20.64.0.0/10, 20.33.0.0/16, 20.128.0.0/16, ... | None | 20.72.193.242 | None | None | None |
IpAddress.util.ip_rev_resolve(ip_addr)
qname | rdtype | response | ip_address | src_row_index | |
---|---|---|---|---|---|
0 | 20.72.193.242 | PTR | None of DNS query names exist: 20.72.193.242., 20.72.193.242.corp.microsoft.com. | 20.72.193.242 | 0 |
IpAddress.util.geoloc(ip_addr)
CountryCode | CountryName | State | Longitude | Latitude | TimeGenerated | Type | IpAddress | |
---|---|---|---|---|---|---|---|---|
0 | US | United States | Washington | -122.3414 | 47.6034 | 2022-04-22 03:03:14.422813 | geolocation | 20.72.193.242 |
IpAddress.ti.lookup_ip(ip_addr)
Ioc | IocType | SafeIoc | QuerySubtype | Provider | Result | Severity | Details | RawResult | Reference | Status | |
---|---|---|---|---|---|---|---|---|---|---|---|
0 | 20.72.193.242 | ipv4 | 20.72.193.242 | None | RiskIQ | True | high | {'summary': {'resolutions': 0, 'certificates': 0, 'malware_hashes': 0, 'projects': 0, 'articles'... | {'summary': {'resolutions': 0, 'certificates': 0, 'malware_hashes': 0, 'projects': 0, 'articles'... | https://community.riskiq.com | 0 |
0 | 20.72.193.242 | ipv4 | 20.72.193.242 | None | Tor | True | information | Not found. | None | https://check.torproject.org/exit-addresses | 0 |
0 | 20.72.193.242 | ipv4 | 20.72.193.242 | None | VirusTotal | True | information | {'verbose_msg': 'IP address in dataset', 'response_code': 1, 'positives': 0, 'detected_urls': []} | {'detected_urls': [], 'asn': 8075, 'country': 'US', 'response_code': 1, 'as_owner': 'MICROSOFT-C... | https://www.virustotal.com/vtapi/v2/ip-address/report | 0 |
0 | 20.72.193.242 | ipv4 | 20.72.193.242 | None | XForce | False | information | Authorization failed. Check account and key details. | <Response [401 Unauthorized]> | https://api.xforce.ibmcloud.com/ipr/20.72.193.242 | 401 |
Notice that we didn't need to worry about either the parameter name or format (more on this in the next section). Also, whatever the function, the output is always returned as a pandas DataFrame.
Data query functions are a little more complex than most other functions and specifically often support many parameters. Rather than try to guess which parameter you meant, we require you to be explicit.
To use a data query, we need to authenticate to the provider.
qry_prov.connect(workspace="CyberSecuritySoc")
We should now have many more data query pivots attached to our entities
Host.pivots()
['MSSentinel_cybersecuritysoc.VMComputer_vmcomputer', 'MSSentinel_cybersecuritysoc.auditd_auditd_all', 'MSSentinel_cybersecuritysoc.az_nsg_interface', 'MSSentinel_cybersecuritysoc.az_nsg_net_flows', 'MSSentinel_cybersecuritysoc.az_nsg_net_flows_depr', 'MSSentinel_cybersecuritysoc.heartbeat', 'MSSentinel_cybersecuritysoc.heartbeat_for_host_depr', 'MSSentinel_cybersecuritysoc.sec_alerts', 'MSSentinel_cybersecuritysoc.sent_bookmarks', 'MSSentinel_cybersecuritysoc.syslog_all_syslog', 'MSSentinel_cybersecuritysoc.syslog_cron_activity', 'MSSentinel_cybersecuritysoc.syslog_logon_failures', 'MSSentinel_cybersecuritysoc.syslog_logons', 'MSSentinel_cybersecuritysoc.syslog_squid_activity', 'MSSentinel_cybersecuritysoc.syslog_sudo_activity', 'MSSentinel_cybersecuritysoc.syslog_user_group_activity', 'MSSentinel_cybersecuritysoc.syslog_user_logon', 'MSSentinel_cybersecuritysoc.wevt_all_events', 'MSSentinel_cybersecuritysoc.wevt_events_by_id', 'MSSentinel_cybersecuritysoc.wevt_get_process_tree', 'MSSentinel_cybersecuritysoc.wevt_list_other_events', 'MSSentinel_cybersecuritysoc.wevt_logon_attempts', 'MSSentinel_cybersecuritysoc.wevt_logon_failures', 'MSSentinel_cybersecuritysoc.wevt_logon_session', 'MSSentinel_cybersecuritysoc.wevt_logons', 'MSSentinel_cybersecuritysoc.wevt_parent_process', 'MSSentinel_cybersecuritysoc.wevt_process_session', 'MSSentinel_cybersecuritysoc.wevt_processes', 'RiskIQ.articles', 'RiskIQ.artifacts', 'RiskIQ.certificates', 'RiskIQ.components', 'RiskIQ.cookies', 'RiskIQ.hostpair_children', 'RiskIQ.hostpair_parents', 'RiskIQ.malware', 'RiskIQ.projects', 'RiskIQ.reputation', 'RiskIQ.resolutions', 'RiskIQ.summary', 'RiskIQ.trackers', 'RiskIQ.whois', 'dns_is_resolvable', 'dns_resolve', 'util.dns_components', 'util.dns_in_abuse_list', 'util.dns_is_resolvable', 'util.dns_resolve', 'util.dns_validate_tld']
If you are not sure of the parameters required by the query you can use the built-in help
Host.MSSentinel_cybersecuritysoc.sec_alerts?
Signature: Host.MSSentinel_cybersecuritysoc.sec_alerts(*args, **kwargs) -> Union[pandas.core.frame.DataFrame, Any] Docstring: Retrieves list of alerts with a common host, account or process Parameters ---------- account_name: str (optional) The account name to find add_query_items: str (optional) Additional query clauses end: datetime Query end time host_name: str (optional) The hostname to find path_separator: str (optional) Path separator (default value is: \\) process_name: str (optional) The process name to find query_project: str (optional) Column project statement (default value is: | project-rename StartTimeUtc = StartTime, EndTim...) start: datetime Query start time table: str (optional) Table name (default value is: SecurityAlert) File: f:\anaconda\envs\msticpy\lib\functools.py Type: function
Host.MSSentinel_cybersecuritysoc.sec_alerts(host_name="victim00").head(5)
TenantId | TimeGenerated | AlertDisplayName | AlertName | Severity | Description | ProviderName | VendorName | VendorOriginalId | SystemAlertId | ResourceId | SourceComputerId | AlertType | ConfidenceLevel | ConfidenceScore | IsIncident | StartTimeUtc | EndTimeUtc | ProcessingEndTime | RemediationSteps | ExtendedProperties | Entities | SourceSystem | WorkspaceSubscriptionId | WorkspaceResourceGroup | ExtendedLinks | ProductName | ProductComponentName | AlertLink | Status | CompromisedEntity | Tactics | Type | Computer | src_hostname | src_accountname | src_procname | host_match | acct_match | proc_match | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 8ecf8077-cf51-4820-aadd-14040956f35d | 2021-03-11 12:05:14.355000+00:00 | Suspected credential theft activity | Suspected credential theft activity | Medium | This program exhibits suspect characteristics potentially associated with credential theft. Onc... | MDATP | Microsoft | da637509097413415122_-841817867 | bf226b1b-8bda-31f7-c848-1f8bbb5f5922 | WindowsDefenderAtp | NaN | False | 2021-03-09 17:56:55.275000+00:00 | 2021-03-09 17:56:55.275000+00:00 | 2021-03-11 12:05:13.759000+00:00 | [\r\n "1. Make sure the machine is completely updated and all your software has the latest patc... | {\r\n "MicrosoftDefenderAtp.Category": "CredentialAccess",\r\n "MicrosoftDefenderAtp.Investiga... | [\r\n {\r\n "$id": "4",\r\n "DnsDomain": "na.contosohotels.com",\r\n "HostName": "vict... | Detection | Microsoft Defender Advanced Threat Protection | https://securitycenter.microsoft.com/alert/da637509097413415122_-841817867?tid=4b2462a4-bbee-495... | New | victim00.na.contosohotels.com | CredentialAccess | SecurityAlert | victim00 | victim00 | True | False | False | |||||||||
1 | 8ecf8077-cf51-4820-aadd-14040956f35d | 2021-03-11 13:24:53.495000+00:00 | 'Mimikatz' hacktool was detected | 'Mimikatz' hacktool was detected | Low | Readily available tools, such as hacking programs, can be used by unauthorized individuals to sp... | MDATP | Microsoft | da637510393722104539_-1180405651 | ef04126b-2683-0a98-d01c-77ee6b1115ac | WindowsDefenderAv | NaN | False | 2021-03-11 06:00:14.083000+00:00 | 2021-03-11 06:00:14.083000+00:00 | 2021-03-11 13:24:53.379000+00:00 | [\r\n "1. Make sure the machine is completely updated and all your software has the latest patc... | {\r\n "MicrosoftDefenderAtp.Category": "Malware",\r\n "MicrosoftDefenderAtp.InvestigationId": ... | [\r\n {\r\n "$id": "4",\r\n "DnsDomain": "na.contosohotels.com",\r\n "HostName": "vict... | Detection | Microsoft Defender Advanced Threat Protection | https://securitycenter.microsoft.com/alert/da637510393722104539_-1180405651?tid=4b2462a4-bbee-49... | New | victim00.na.contosohotels.com | Unknown | SecurityAlert | victim00 | victim00 | True | False | False | |||||||||
2 | 8ecf8077-cf51-4820-aadd-14040956f35d | 2021-03-11 13:24:53.490000+00:00 | Suspected credential theft activity | Suspected credential theft activity | Medium | This program exhibits suspect characteristics potentially associated with credential theft. Onc... | MDATP | Microsoft | da637509097413415122_-841817867 | bf226b1b-8bda-31f7-c848-1f8bbb5f5922 | WindowsDefenderAtp | NaN | False | 2021-03-09 17:56:55.275000+00:00 | 2021-03-09 17:56:55.275000+00:00 | 2021-03-11 13:24:53.363000+00:00 | [\r\n "1. Make sure the machine is completely updated and all your software has the latest patc... | {\r\n "MicrosoftDefenderAtp.Category": "CredentialAccess",\r\n "MicrosoftDefenderAtp.Investiga... | [\r\n {\r\n "$id": "4",\r\n "DnsDomain": "na.contosohotels.com",\r\n "HostName": "vict... | Detection | Microsoft Defender Advanced Threat Protection | https://securitycenter.microsoft.com/alert/da637509097413415122_-841817867?tid=4b2462a4-bbee-495... | New | victim00.na.contosohotels.com | CredentialAccess | SecurityAlert | victim00 | victim00 | True | False | False | |||||||||
3 | 8ecf8077-cf51-4820-aadd-14040956f35d | 2021-03-11 13:19:42.521000+00:00 | Malicious credential theft tool execution detected | Malicious credential theft tool execution detected | High | A known credential theft tool execution command line was detected.\nEither the process itself or... | MDATP | Microsoft | da637508847019595161_-562481393 | 753680a5-4d20-2726-61b4-9c36e620ea26 | WindowsDefenderAtp | NaN | False | 2021-03-09 10:56:58.134000+00:00 | 2021-03-09 10:56:58.134000+00:00 | 2021-03-11 13:19:42.289000+00:00 | [\r\n "1. Make sure the machine is completely updated and all your software has the latest patc... | {\r\n "MicrosoftDefenderAtp.Category": "CredentialAccess",\r\n "MicrosoftDefenderAtp.Investiga... | [\r\n {\r\n "$id": "4",\r\n "DnsDomain": "na.contosohotels.com",\r\n "HostName": "vict... | Detection | Microsoft Defender Advanced Threat Protection | https://securitycenter.microsoft.com/alert/da637508847019595161_-562481393?tid=4b2462a4-bbee-495... | New | victim00.na.contosohotels.com | CredentialAccess | SecurityAlert | victim00 | victim00 | True | False | False | |||||||||
4 | 8ecf8077-cf51-4820-aadd-14040956f35d | 2021-03-11 14:30:14.730000+00:00 | 'Mimikatz' hacktool was detected | 'Mimikatz' hacktool was detected | Low | Readily available tools, such as hacking programs, can be used by unauthorized individuals to sp... | MDATP | Microsoft | da637510393722104539_-1180405651 | ef04126b-2683-0a98-d01c-77ee6b1115ac | WindowsDefenderAv | NaN | False | 2021-03-11 06:00:14.083000+00:00 | 2021-03-11 06:00:14.083000+00:00 | 2021-03-11 14:30:14.450000+00:00 | [\r\n "1. Make sure the machine is completely updated and all your software has the latest patc... | {\r\n "MicrosoftDefenderAtp.Category": "Malware",\r\n "MicrosoftDefenderAtp.InvestigationId": ... | [\r\n {\r\n "$id": "4",\r\n "DnsDomain": "na.contosohotels.com",\r\n "HostName": "vict... | Detection | Microsoft Defender Advanced Threat Protection | https://securitycenter.microsoft.com/alert/da637510393722104539_-1180405651?tid=4b2462a4-bbee-49... | New | victim00.na.contosohotels.com | Unknown | SecurityAlert | victim00 | victim00 | True | False | False |
Pivot.browse()
VBox(children=(HBox(children=(VBox(children=(HTML(value='<b>Entities</b>'), Select(description='entity', layou…
Due to various factors (historical, underlying data, developer laziness and forgetfullness, etc.) the functionality in MSTICPy can be inconsistent in the way it uses input parameters.
Also, many functions will only accept inputs as a single value, or a list or a DataFrame or some unpredictable combination of these.
Pivot functions allow you to largely forget about this - you can use the same function whether you have:
Let's take an example.
Suppose we have a set of IP addresses pasted from somewhere that we want to use as input.
We need to convert this into a Python data object of some sort.
To do this we can use another Pivot utility %%txt2df
. This is a
Jupyter/IPython magic function so you can just paste you data in
a cell.
Use %%txt2df --help
in an empty cell to see the full syntax.
The example below we specify a comma separator, that the data has a headers row and to save the converted data as a DataFrame named "ip_df".
Warning this will overwrite any existing variable of this
name
%%txt2df --sep , --headers --name ip_df
idx, ip, type
0, 172.217.15.99, Public
1, 40.85.232.64, Public
2, 20.38.98.100, Public
3, 23.96.64.84, Public
4, 65.55.44.108, Public
5, 131.107.147.209, Public
6, 10.0.3.4, Private
7, 10.0.3.5, Private
8, 13.82.152.48, Public
idx | ip | type | |
---|---|---|---|
0 | 0 | 172.217.15.99 | Public |
1 | 1 | 40.85.232.64 | Public |
2 | 2 | 20.38.98.100 | Public |
3 | 3 | 23.96.64.84 | Public |
4 | 4 | 65.55.44.108 | Public |
5 | 5 | 131.107.147.209 | Public |
6 | 6 | 10.0.3.4 | Private |
7 | 7 | 10.0.3.5 | Private |
8 | 8 | 13.82.152.48 | Public |
For our example we'll also create a standard Python list from the ip column.
ip_list = list(ip_df.ip)
print(ip_list)
['172.217.15.99', '40.85.232.64', '20.38.98.100', '23.96.64.84', '65.55.44.108', '131.107.147.209', '10.0.3.4', '10.0.3.5', '13.82.152.48']
If you recall the earlier example of get_ip_type
, passing it
a list or DataFrame doesn't result in anything useful.
get_ip_type(ip_list)
['172.217.15.99', '40.85.232.64', '20.38.98.100', '23.96.64.84', '65.55.44.108', '131.107.147.209', '10.0.3.4', '10.0.3.5', '13.82.152.48'] does not appear to be an IPv4 or IPv6 address
'Unspecified'
The pivotized version of get_ip_type can accept and correctly process a list
IpAddress.util.ip_type(ip_list)
ip | result | src_row_index | |
---|---|---|---|
0 | 172.217.15.99 | Public | 0 |
1 | 40.85.232.64 | Public | 1 |
2 | 20.38.98.100 | Public | 2 |
3 | 23.96.64.84 | Public | 3 |
4 | 65.55.44.108 | Public | 4 |
5 | 131.107.147.209 | Public | 5 |
6 | 10.0.3.4 | Private | 6 |
7 | 10.0.3.5 | Private | 7 |
8 | 13.82.152.48 | Public | 8 |
When using a DataFrame as an input to pivot, things are a little more complicated. We have to pass the DataFrame to the function and also supply the name of the column thatcontains the input data.
IpAddress.util.whois(ip_df, column="ip")
nir | asn_registry | asn | asn_cidr | asn_country_code | asn_date | asn_description | query | nets | raw | referral | raw_referral | |
---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | NaN | arin | 15169 | 172.217.15.0/24 | US | 2012-04-16 | GOOGLE, US | 172.217.15.99 | [{'cidr': '172.217.0.0/16', 'name': 'GOOGLE', 'handle': 'NET-172-217-0-0-1', 'range': '172.217.0... | NaN | NaN | NaN |
1 | NaN | arin | 8075 | 40.80.0.0/12 | US | 2015-02-23 | MICROSOFT-CORP-MSN-AS-BLOCK, US | 40.85.232.64 | [{'cidr': '40.80.0.0/12, 40.124.0.0/16, 40.74.0.0/15, 40.76.0.0/14, 40.120.0.0/14, 40.125.0.0/17... | NaN | NaN | NaN |
2 | NaN | arin | 8075 | 20.36.0.0/14 | US | 2017-10-18 | MICROSOFT-CORP-MSN-AS-BLOCK, US | 20.38.98.100 | [{'cidr': '20.128.0.0/16, 20.33.0.0/16, 20.34.0.0/15, 20.36.0.0/14, 20.64.0.0/10, 20.40.0.0/13, ... | NaN | NaN | NaN |
3 | NaN | arin | 8075 | 23.96.0.0/14 | US | 2013-06-18 | MICROSOFT-CORP-MSN-AS-BLOCK, US | 23.96.64.84 | [{'cidr': '23.96.0.0/13', 'name': 'MSFT', 'handle': 'NET-23-96-0-0-1', 'range': '23.96.0.0 - 23.... | NaN | NaN | NaN |
4 | NaN | arin | 8075 | 65.52.0.0/14 | US | 2001-02-14 | MICROSOFT-CORP-MSN-AS-BLOCK, US | 65.55.44.108 | [{'cidr': '65.52.0.0/14', 'name': 'MICROSOFT-1BLK', 'handle': 'NET-65-52-0-0-1', 'range': '65.52... | NaN | NaN | NaN |
5 | NaN | arin | 3598 | 131.107.0.0/16 | US | 1988-11-11 | MICROSOFT-CORP-AS, US | 131.107.147.209 | [{'cidr': '131.107.0.0/16', 'name': 'MICROSOFT', 'handle': 'NET-131-107-0-0-1', 'range': '131.10... | NaN | NaN | NaN |
6 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
7 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
8 | NaN | arin | 8075 | 13.64.0.0/11 | US | 2015-03-26 | MICROSOFT-CORP-MSN-AS-BLOCK, US | 13.82.152.48 | [{'cidr': '13.64.0.0/11, 13.96.0.0/13, 13.104.0.0/14', 'name': 'MSFT', 'handle': 'NET-13-64-0-0-... | NaN | NaN | NaN |
Note: for most functions you can ignore the parameter
name and just specify it as a positional parameter. You can also use the original parameter name of the underlying function or the placeholder name "value".
The following are all equivalent:
IpAddress.util.ip_type(ip_list)
IpAddress.util.ip_type(ip_str=ip_list)
IpAddress.util.ip_type(value=ip_list)
IpAddress.util.ip_type(data=ip_list)
When passing both a DataFrame and column name use:
IpAddress.util.ip_type(data=ip_df, column="col_name")
You can also pass an entity instance of an entity as a input parameter. The pivot code knows which attribute or attributes of an entity will provider the input value.
ip_entity = IpAddress(Address="40.85.232.64")
IpAddress.util.ip_type(ip_entity)
ip | result | |
---|---|---|
0 | 40.85.232.64 | Public |
Many of the underlying functions only accept single values as inputs. Examples of these are the data query functions - typically they expect a single host name, IP address, etc.
Pivot knows about the type of parameters that the function accepts. It will adjust the input to match the expectations of the underlying function. If a list or DataFrame is passed as input to a single-value function Pivot will split the input and call the function once for each value. It then combines the output into a single DataFrame before returning the results.
You can read a bit more about how this is done in the Appendix TODO
The Pivot class has a buit-in time range. This is used by default for all queries. Don't worry - you can change it easily
mp.pivot.timespan
TimeSpan(start=2022-06-08 22:10:15.959575+00:00, end=2022-06-09 22:10:15.959575+00:00, period=1 day, 0:00:00)
You can edit the time range interactively
mp.pivot.edit_query_time()
VBox(children=(HTML(value='<h4>Set time range for pivot functions.</h4>'), HBox(children=(DatePicker(value=dat…
Or by setting the timespan property directly
from msticpy.common.timespan import TimeSpan
# TimeSpan accepts datetimes or datestrings
timespan = TimeSpan(start="02/01/2021", end="02/15/2021")
mp.pivot.timespan = timespan
There is also a convenience function for setting the time directly with Python datetimes or date strings
mp.pivot.current.set_timespan(start="2020-02-06 03:00:00", end="2021-02-15 01:42:42")
You can also override the built-in time settings by specifying
start
and end
as parameters.
dt1 = mp.pivot.timespan.start
dt2 = mp.pivot.timespan.end
Host.MSSentinel_cybersecuritysoc.sec_alerts(host_name="victim00", start=dt1, end=dt2)
TenantId | TimeGenerated | AlertDisplayName | AlertName | Severity | Description | ProviderName | VendorName | VendorOriginalId | SystemAlertId | ResourceId | SourceComputerId | AlertType | ConfidenceLevel | ConfidenceScore | IsIncident | StartTimeUtc | EndTimeUtc | ProcessingEndTime | RemediationSteps | ExtendedProperties | Entities | SourceSystem | WorkspaceSubscriptionId | WorkspaceResourceGroup | ExtendedLinks | ProductName | ProductComponentName | AlertLink | Status | CompromisedEntity | Tactics | Techniques | Type | Computer | src_hostname | src_accountname | src_procname | host_match | acct_match | proc_match |
---|
The Pivot layer will pass any unused keyword parameters to the
underlying function. This does not usually apply to positional parameters -
if you want parameters to get to the function, you have to name them
explicitly.
In this example the add_query_items
parameter is passed to the underlying
query function
Host.MSSentinel_cybersecuritysoc.wevt_logons(
host_name="victimPc",
add_query_items="| summarize count() by LogonType"
)
LogonType | count_ | |
---|---|---|
0 | 5 | 21650 |
1 | 3 | 6808 |
2 | 4 | 9426 |
3 | 2 | 109 |
4 | 10 | 44 |
5 | 0 | 7 |
6 | 9 | 8 |
Because all pivot functions accept DataFrames as input and produce DataFrames as output, it means that it is possible to chain pivot functions into a pipeline.
You can join the input to the output. This usually only makes sense when the input is a DataFrame. It lets you keep the previously accumumated results and tag on the additional columns produced by the pivot function you are calling.
The join
parameter supports "inner", "left", "right" and "outer"
joins (be careful with the latter though!)
See pivot joins documentation
Although joining is useful in pipelines you can use it on any function whether in a pipeline or not.
IpAddress.util.whois(ip_df, column="ip", join="inner")
idx | ip | type | nir | asn_registry | asn | asn_cidr | asn_country_code | asn_date | asn_description | query | nets | raw | referral | raw_referral | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 0 | 172.217.15.99 | Public | NaN | arin | 15169 | 172.217.15.0/24 | US | 2012-04-16 | GOOGLE, US | 172.217.15.99 | [{'cidr': '172.217.0.0/16', 'name': 'GOOGLE', 'handle': 'NET-172-217-0-0-1', 'range': '172.217.0... | NaN | NaN | NaN |
1 | 1 | 40.85.232.64 | Public | NaN | arin | 8075 | 40.80.0.0/12 | US | 2015-02-23 | MICROSOFT-CORP-MSN-AS-BLOCK, US | 40.85.232.64 | [{'cidr': '40.80.0.0/12, 40.124.0.0/16, 40.74.0.0/15, 40.76.0.0/14, 40.120.0.0/14, 40.125.0.0/17... | NaN | NaN | NaN |
2 | 2 | 20.38.98.100 | Public | NaN | arin | 8075 | 20.36.0.0/14 | US | 2017-10-18 | MICROSOFT-CORP-MSN-AS-BLOCK, US | 20.38.98.100 | [{'cidr': '20.128.0.0/16, 20.33.0.0/16, 20.34.0.0/15, 20.36.0.0/14, 20.64.0.0/10, 20.40.0.0/13, ... | NaN | NaN | NaN |
3 | 3 | 23.96.64.84 | Public | NaN | arin | 8075 | 23.96.0.0/14 | US | 2013-06-18 | MICROSOFT-CORP-MSN-AS-BLOCK, US | 23.96.64.84 | [{'cidr': '23.96.0.0/13', 'name': 'MSFT', 'handle': 'NET-23-96-0-0-1', 'range': '23.96.0.0 - 23.... | NaN | NaN | NaN |
4 | 4 | 65.55.44.108 | Public | NaN | arin | 8075 | 65.52.0.0/14 | US | 2001-02-14 | MICROSOFT-CORP-MSN-AS-BLOCK, US | 65.55.44.108 | [{'cidr': '65.52.0.0/14', 'name': 'MICROSOFT-1BLK', 'handle': 'NET-65-52-0-0-1', 'range': '65.52... | NaN | NaN | NaN |
5 | 5 | 131.107.147.209 | Public | NaN | arin | 3598 | 131.107.0.0/16 | US | 1988-11-11 | MICROSOFT-CORP-AS, US | 131.107.147.209 | [{'cidr': '131.107.0.0/16', 'name': 'MICROSOFT', 'handle': 'NET-131-107-0-0-1', 'range': '131.10... | NaN | NaN | NaN |
6 | 8 | 13.82.152.48 | Public | NaN | arin | 8075 | 13.64.0.0/11 | US | 2015-03-26 | MICROSOFT-CORP-MSN-AS-BLOCK, US | 13.82.152.48 | [{'cidr': '13.64.0.0/11, 13.96.0.0/13, 13.104.0.0/14', 'name': 'MSFT', 'handle': 'NET-13-64-0-0-... | NaN | NaN | NaN |
Pivot pipelines are implemented pandas customr accessors. Read more about Extending pandas here
When you load Pivot it adds the mp_pivot
accessor to the pandas
DataFrame
class. This
appears as an attribute to DataFrames.
>>> ips_df.mp_pivot
<msticpy.datamodel.pivot_pd_accessor.PivotAccessor at 0x275754e2208>
The main pipelining function run
is a method of mp_pivot
.
run
requires two parameters - the pivot function to run and
the column to use as input. See mp_pivot.run documentation
# Create a dataframe for input
ip_list = [
"192.168.40.32",
"192.168.1.216",
"192.168.153.17",
"3.88.48.125",
"10.200.104.20",
"192.168.90.101",
"192.168.150.50",
"172.16.100.31",
"192.168.30.189",
"10.100.199.10",
]
ips_df = pd.DataFrame(ip_list, columns=["IP"])
Here is an example of using mp_pivot
to call 4 pivot functions, each
using the output of the previous function as input and using
the join
parameter to accumulate the results from each
stage.
Let's step through it line by line.
ips_df
- this is just the starting DataFrame, our input data.mp_pivot.run()
accessor method on this dataframe.
We pass it the pivot function that we want to run and the input column name.
This column name is the column in ips_df where our input IP addresses are.
We've also specified an join
type of inner. In this case the join type doesn't
really matter since we know we get exactly one output row for every input row.query
function to filter out unwanted entries
from the previous stage. In this case we only want Public IP addresses.
This illustrates that you can intersperse standard pandas functions
in the same pipeline. We could have also added a column selector expression
([["col1", "col2"...]]) if we wanted to filter the columns passed to the
next stagewhois
. Remember the "column" parameter
always refers to the input column, i.e. the column from previous stage
that we want to use in this stage.geoloc
to get geo location details joining with a left
join - this preserves the input data rows and adds null columns in any cases
where the pivot function returned no result..head(5)
shown here).(
ips_df
.mp_pivot.run(IpAddress.util.ip_type, column="IP", join="inner")
.query("result == 'Public'").head(10)
.mp_pivot.run(IpAddress.util.whois, column="ip", join="left")
.mp_pivot.run(IpAddress.util.geoloc, column="ip", join="left")
.mp_pivot.run(IpAddress.MSSentinel_cybersecuritysoc.sec_list_alerts_for_ip, source_ip_list="ip", join="left")
).head(5)
IP | ip | result | asn | asn_cidr | asn_country_code | asn_date | asn_description | asn_registry | nets | nir | query | raw | raw_referral | referral | CountryCode | CountryName | State | City | Longitude | Latitude | Asn | edges | Type_x | AdditionalData | ... | AlertType | ConfidenceLevel | ConfidenceScore | IsIncident | StartTimeUtc | EndTimeUtc | ProcessingEndTime | RemediationSteps | ExtendedProperties | Entities | SourceSystem | WorkspaceSubscriptionId | WorkspaceResourceGroup | ExtendedLinks | ProductName | ProductComponentName | AlertLink | Status | CompromisedEntity | Tactics | Type_y | SystemAlertId1 | ExtendedProperties1 | Entities1 | MatchingIps | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 3.88.48.125 | 3.88.48.125 | Public | 14618 | 3.80.0.0/12 | US | 2017-12-20 | AMAZON-AES, US | arin | [{'cidr': '3.0.0.0/9', 'name': 'AT-88-Z', 'handle': 'NET-3-0-0-0-1', 'range': '3.0.0.0 - 3.127.2... | None | 3.88.48.125 | None | None | None | US | United States | Virginia | Ashburn | -77.4728 | 39.0481 | None | {} | geolocation | {} | ... | 8ecf8077-cf51-4820-aadd-14040956f35d_8a369bd2-97b6-4fe2-922a-cd170faf25bc | NaN | False | 2020-12-19 13:04:59+00:00 | 2020-12-19 19:04:59+00:00 | 2020-12-19 19:10:17+00:00 | {\r\n "Query": "// The query_now parameter (in UTC format) was prepended to the query to reflec... | [\r\n {\r\n "$id": "3",\r\n "Address": "3.88.48.125",\r\n "Type": "ip"\r\n }\r\n] | Detection | d1d8779d-38d7-4f06-91db-9cbc8de0176f | soc | Azure Sentinel | Scheduled Alerts | New | CommandAndControl | SecurityAlert | fdc54c12-efba-38b0-8379-f06d7fbfd34a | {\r\n "Query": "// The query_now parameter (in UTC format) was prepended to the query to reflec... | [\r\n {\r\n "$id": "3",\r\n "Address": "3.88.48.125",\r\n "Type": "ip"\r\n }\r\n] | [3.88.48.125] | |||||
1 | 3.88.48.125 | 3.88.48.125 | Public | 14618 | 3.80.0.0/12 | US | 2017-12-20 | AMAZON-AES, US | arin | [{'cidr': '3.0.0.0/9', 'name': 'AT-88-Z', 'handle': 'NET-3-0-0-0-1', 'range': '3.0.0.0 - 3.127.2... | None | 3.88.48.125 | None | None | None | US | United States | Virginia | Ashburn | -77.4728 | 39.0481 | None | {} | geolocation | {} | ... | ThreatIntelligence | 83 | NaN | False | 2020-12-23 13:48:23+00:00 | 2020-12-23 13:48:23+00:00 | 2020-12-23 14:08:15+00:00 | {\r\n "Query": "CommonSecurityLog| where RequestURL hasprefix(\"www.arboretum.hu\") | where Tim... | [\r\n {\r\n "$id": "3",\r\n "DnsDomain": "www.arboretum.hu",\r\n "HostName": "www.arbo... | Detection | d1d8779d-38d7-4f06-91db-9cbc8de0176f | soc | Azure Sentinel | Microsoft Threat Intelligence Analytics | New | 3.88.48.125 | Unknown | SecurityAlert | 625ff9af-dddc-0cf8-9d4b-e79067fa2e71 | {\r\n "Query": "CommonSecurityLog| where RequestURL hasprefix(\"www.arboretum.hu\") | where Tim... | [\r\n {\r\n "$id": "3",\r\n "DnsDomain": "www.arboretum.hu",\r\n "HostName": "www.arbo... | [3.88.48.125] | |||
2 | 3.88.48.125 | 3.88.48.125 | Public | 14618 | 3.80.0.0/12 | US | 2017-12-20 | AMAZON-AES, US | arin | [{'cidr': '3.0.0.0/9', 'name': 'AT-88-Z', 'handle': 'NET-3-0-0-0-1', 'range': '3.0.0.0 - 3.127.2... | None | 3.88.48.125 | None | None | None | US | United States | Virginia | Ashburn | -77.4728 | 39.0481 | None | {} | geolocation | {} | ... | ThreatIntelligence | 83 | NaN | False | 2020-12-23 13:48:23+00:00 | 2020-12-23 13:48:23+00:00 | 2020-12-23 14:08:15+00:00 | {\r\n "Query": "CommonSecurityLog| where RequestURL hasprefix(\"www.arboretum.hu\") | where Tim... | [\r\n {\r\n "$id": "3",\r\n "DnsDomain": "www.arboretum.hu",\r\n "HostName": "www.arbo... | Detection | d1d8779d-38d7-4f06-91db-9cbc8de0176f | soc | Azure Sentinel | Microsoft Threat Intelligence Analytics | New | 3.88.48.125 | Unknown | SecurityAlert | c977f904-ab30-d57e-986f-9d6ebf72771b | {\r\n "Query": "CommonSecurityLog| where RequestURL hasprefix(\"www.arboretum.hu\") | where Tim... | [\r\n {\r\n "$id": "3",\r\n "DnsDomain": "www.arboretum.hu",\r\n "HostName": "www.arbo... | [3.88.48.125] | |||
3 | 3.88.48.125 | 3.88.48.125 | Public | 14618 | 3.80.0.0/12 | US | 2017-12-20 | AMAZON-AES, US | arin | [{'cidr': '3.0.0.0/9', 'name': 'AT-88-Z', 'handle': 'NET-3-0-0-0-1', 'range': '3.0.0.0 - 3.127.2... | None | 3.88.48.125 | None | None | None | US | United States | Virginia | Ashburn | -77.4728 | 39.0481 | None | {} | geolocation | {} | ... | ThreatIntelligence | 83 | NaN | False | 2020-12-23 13:48:23+00:00 | 2020-12-23 13:48:23+00:00 | 2020-12-23 14:08:15+00:00 | {\r\n "Query": "CommonSecurityLog| where RequestURL hasprefix(\"www.arboretum.hu\") | where Tim... | [\r\n {\r\n "$id": "3",\r\n "DnsDomain": "www.arboretum.hu",\r\n "HostName": "www.arbo... | Detection | d1d8779d-38d7-4f06-91db-9cbc8de0176f | soc | Azure Sentinel | Microsoft Threat Intelligence Analytics | New | 3.88.48.125 | Unknown | SecurityAlert | 9ee547e4-cba1-47d1-e1f9-87247b693a52 | {\r\n "Query": "CommonSecurityLog| where RequestURL hasprefix(\"www.arboretum.hu\") | where Tim... | [\r\n {\r\n "$id": "3",\r\n "DnsDomain": "www.arboretum.hu",\r\n "HostName": "www.arbo... | [3.88.48.125] | |||
4 | 3.88.48.125 | 3.88.48.125 | Public | 14618 | 3.80.0.0/12 | US | 2017-12-20 | AMAZON-AES, US | arin | [{'cidr': '3.0.0.0/9', 'name': 'AT-88-Z', 'handle': 'NET-3-0-0-0-1', 'range': '3.0.0.0 - 3.127.2... | None | 3.88.48.125 | None | None | None | US | United States | Virginia | Ashburn | -77.4728 | 39.0481 | None | {} | geolocation | {} | ... | ThreatIntelligence | 83 | NaN | False | 2020-12-23 13:48:23+00:00 | 2020-12-23 13:48:23+00:00 | 2020-12-23 14:08:16+00:00 | {\r\n "Query": "CommonSecurityLog| where RequestURL hasprefix(\"www.arboretum.hu\") | where Tim... | [\r\n {\r\n "$id": "3",\r\n "DnsDomain": "www.arboretum.hu",\r\n "HostName": "www.arbo... | Detection | d1d8779d-38d7-4f06-91db-9cbc8de0176f | soc | Azure Sentinel | Microsoft Threat Intelligence Analytics | New | 3.88.48.125 | Unknown | SecurityAlert | 83a0e08a-1adb-ef75-1c56-f6c9ce25ca69 | {\r\n "Query": "CommonSecurityLog| where RequestURL hasprefix(\"www.arboretum.hu\") | where Tim... | [\r\n {\r\n "$id": "3",\r\n "DnsDomain": "www.arboretum.hu",\r\n "HostName": "www.arbo... | [3.88.48.125] |
5 rows × 63 columns
In addition to run
, the mp_pivot
accessor also
has the following functions:
display
- this simply displays the data at the point called in
the pipeline. You can add an optional title, filtering and the number
or rows to displaytee
- this forks a copy of the dataframe at the point it is
called in the pipeline. It will assign the forked copy to the name
given in the var_name
parameter. If there is an existing variable of
the same name it will not overwrite it unless you add the clobber=True
parameter.In both cases the pipelined data is passed through unchanged. See Pivot functions help for more details.
Use of these is shown below
...
.mp_pivot.run(entities.IpAddress.util.geoloc, column="ip", join="left")
.mp_pivot.display(title="Geo Lookup", cols=["IP", "City"]) # << display an intermediate result
.mp_pivot.tee(var_name="geoip_df", clobber=True) # << save a copy called 'geoip_df'
.mp_pivot.run(entities.IpAddress.AzureSentinel.SecurityAlert_list_alerts_for_ip, source_ip_list="ip", join="left")
In the next release we've also implemented:
tee_exec
- this executes a function on a forked copy of the DataFrame
The function must be a pandas function or custom accessor. A
good example of the use of this might be creating a plot or summary
table to display partway through the pipeline.You can add pivot functions of your own. You need to supply:
Full details of this are described here.
from hashlib import md5
def my_func2(input: str):
md5_hash = "-".join(hex(b)[2:] for b in md5("hello".encode("utf-8")).digest())
return {
"Title": input.upper(),
"Hash": md5_hash
}
mp.Pivot.add_pivot_function(
func=my_func2,
container="cyber", # which container it will appear in on the entity
input_type="value",
entity_map={"Host": "HostName"},
func_input_value_arg="input",
func_new_name="il_upper_hash_name",
)
Host.cyber.il_upper_hash_name("host_name")
We've taken a short tour through the MSTICPy looking at how they make the functionality in the package easier to discover and use. I'm particularly excited about the pipeline functionality. In the next release we're going to make it possible to define reusable pipelines in configuration files and execute them with a single function call. This should help streamline some common patterns in notebooks for Cyber hunting and investigation.
Please send any feedback or suggestions for improvements to msticpy@microsoft.com or create an issue on https://github.com/microsoft/msticpy.
Happy hunting!
In Python you can create functions that return other functions. On the way they can change how the arguments and output are processed.
Take this simple function that just applies proper capitalization to an input string.
def print_me(arg):
print(arg.capitalize())
print_me("hello")
Hello
If we try to pass a list to this function we get an
expected exception about lists not supporting capitalize
print_me(["hello", "world"])
--------------------------------------------------------------------------- NameError Traceback (most recent call last) <ipython-input-66-94b3e61eb86f> in <module> ----> 1 print_me(["hello", "world"]) NameError: name 'print_me' is not defined
We could create a wrapper function that checked the input and iterated over the individual items if arg is a list. The works but we don't want to have to do this for every function that we want to have flexible input!
def print_me_list(arg):
if isinstance(arg, list):
for item in arg:
print_me(item)
else:
print_me(arg)
print_me_list("hello")
print_me_list(["how", "are", "you", "?"])
Hello How Are You ?
Instead we can create a function wrapper. The outer function
dont_care_func
defines an inner function, list_or_str
and then
returns this function. The inner function list_or_str
is what
implements the same "is-this-a-string-or-list" logic that we
saw in the previous example.
Crucially though, it isn't hard-coded to call print_me
but
calls whatever function passed to it from the outer function
dont_care_func
.
# Our magic wrapper
def dont_care_func(func):
def list_or_str(arg):
if isinstance(arg, list):
for item in arg:
func(item)
else:
func(arg)
return list_or_str
How do we use this?
We simply pass the function that we want to wrap to
dont_care_func
. Recall, that this function just returns
an instance of the inner function. In this particular instance
the value func
will have been replaced by the actual function
print_me
.
print_stuff = dont_care_func(print_me)
Now we have a wrapped version of print_me
that can
handle different types of input. Magic!
print_stuff("hello")
print_stuff(["how", "are", "you", "?"])
Hello How Are You ?
We can also define further functions and create wrapped
versions of those by passing them to dont_care_func
.
def shout_me(arg):
print(arg.upper(), "\U0001F92C!", end=" ")
shout_stuff = dont_care_func(shout_me)
shout_stuff("hello")
shout_stuff(["how", "are", "you", "?"])
HELLO 🤬! HOW 🤬! ARE 🤬! YOU 🤬! ? 🤬!
The wrapper functionality in Pivot is a bit more complex than this but essentially operates this way.