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Deploy the asim testing tool

PUBLIC PREVIEW OF ASIM PARSER DEVELOPMENT FOR ENHANCED SECURITY INFORMATION
MODEL

Author: Edward Hargrove Date: 2023-03-29

This article focuses on the development of ASIM parsers. To learn more about
ASIM parsers, you can refer to the ASIM parsers overview, usage, management, and
list. Additionally, you can gain general knowledge about ASIM by watching the
Deep Dive Webinar on Microsoft Sentinel Normalizing Parsers and Normalized
Content or reviewing the slides, exploring the ASIM overview, schemas, and
content. If you are experiencing unusual behavior with your Fortinet Fortigate
events in Azure Sentinel, you can contribute your parsers by following these
steps: first, develop both a filtering parser and a parameter-less parser, then
create a YAML file for the parser according to the instructions in Deploying
Parsers.

Table of contents
* Develop Advanced Security Information Model (ASIM) parsers (Public preview)
* Azure Sentinel Fortinet Parser
* Parse text data in Azure Monitor logs
* Where can I find more information about Microsoft Sentinel data connectors?
* How do I integrate Azure Sentinel with commonsecuritylog?
* Where can I find the workspaceid and workspacekey parameters in Sentinel?
* How do I integrate Microsoft Sentinel with Zimperium?

DEVELOP ADVANCED SECURITY INFORMATION MODEL (ASIM) PARSERS (PUBLIC PREVIEW)

Note

Microsoft Sentinel, formerly known as Azure Sentinel, will have its pages
updated in the upcoming weeks. Discover additional information regarding the
latest security improvements by Microsoft.

ASIM users make use of unified parsers to query data in a normalized format and
to ensure that all relevant data is included in the query. Instead of using
table names, these parsers handle the specific details of each source by
utilizing source-specific parsers.

Microsoft Sentinel includes pre-built parsers that are tailored to specific data
sources. There may be instances where you need to customize or create new
parsers for these data sources.

* When your device provides events that fit an ASIM schema, but a
source-specific parser for your device and the relevant schema is not
available in Microsoft Sentinel.

* In case your device doesn't send events in the expected format by ASIM
parsers, even if ASIM source-specific parsers are available for your device.
This can happen when your device uses a different method or format to send
events. For instance:

* Your source device may be configured to send events in a non-standard way.

* Your device may have a different version than the one supported by the ASIM
parser.

* The events might be collected, modified, and forwarded by an intermediary
system.

Consult the ASIM architecture diagram to gain an understanding of where parsers
fit into the overall architecture.

Important

ASIM is presently in its preview phase, which means that it falls under the
Azure Preview Supplemental Terms. These terms consist of further legal
conditions that are applicable to Azure features that are in beta, preview, or
still not available for general use.

CUSTOM PARSER DEVELOPMENT PROCESS

The outlined procedure outlines the major stages involved in creating a
personalized ASIM and a parser specific to the source.

1. Collect sample logs.

3. Identify the schemas or schemas that the events sent from the source
represent. For more information, see Schema overview.

5. Map the source event fields to the identified schema or schemas.

7. Develop one or more ASIM parsers for your source. You'll need to develop a
filtering parser and a parameter- less parser for each schema relevant to
the source.

8. Test your parser.

10. Deploy the parsers into your Microsoft Sentinel workspaces.

11.

12. Update the relevant ASIM unifying parser to reference the new custom
parser. For more information, see Managing ASIM parsers.

13.

14. You might also want to contribute your parsers to the primary ASIM
distribution. Contributed parsers may also be made available in all
workspaces as built-in parsers.

This article takes you through the steps involved in developing, testing, and
deploying a process.

Tip

Check out the Deep Dive Webinar regarding Microsoft Sentinel's Normalizing
Parsers and Normalized Content, or you can refer to the corresponding slide
deck. To learn more, please check the Next steps section.

COLLECT SAMPLE LOGS

In order to construct efficient ASIM parsers, it is essential to have a diverse
collection of logs that accurately reflect the system. Typically, this
necessitates the establishment of the source system and its connection to
Microsoft Sentinel. However, in the absence of the source device, cloud-based
pay-as-you-go services can be employed to deploy numerous devices for
development and testing purposes.

Furthermore, obtaining the vendor's documentation and log samples can expedite
the development process and minimize errors by ensuring comprehensive coverage
of the log format.

An inclusive collection of logs should consist of:

* Events that produce varied outcomes.
* Occurrences that require distinct reaction measures.
* There is a need for value normalization in various fields, including
usernames, hostnames, IDs, and other formats.

Tip

To initiate a new custom parser for a particular schema, it is crucial to
utilize an already existing parser. The significance of using an existing parser
lies in the fact that it ensures that the filtering parsers accept all the
necessary parameters of the schema.

MAPPING

Prior to creating a parser, align the data present in the source event or events
with the identified schema.

* Map all the mandatory field s and if possible, include the suggested fields.
* Attempt to associate all accessible information from the source with
standardized fields. If the information is not included in the selected
schema, then contemplate mapping it to fields present in alternative schemas.
* The ASIM permits only normalized values for fields at the source. The
original value can be found in a distinct field, like
EventOriginalResultDetails .

DEVELOPING PARSERS

Produce parsers for each relevant schema, including both filtering and
parameterless options.

A parser query with three components is a KQL query that is created within the
Microsoft Sentinel Logs page.

Refine > Analyze > Organize Data Fields

FILTERING

FILTERING THE RELEVANT RECORDS

A Microsoft Sentinel table often comprises various kinds of events, exemplified
by:

* The data in the Syslog table originates from various sources.
* Custom tables have the capability to contain data from a singular source that
can accommodate multiple schemas and event types.

Hence, the initial step for a parser is to screen out solely the entries that
pertain to the desired schema.

KQL utilizes the operator referred to as where for filtering purposes. As an
illustration, the creation of a process is reported in Sysmon event 1, which is
then standardized to conform to the ProcessEvent schema. To filter the Sysmon
event 1, which is included in the Event table, the filter below should be
employed:

Event | where Source == "Microsoft-Windows-Sysmon" and EventID == 1

Important

It is not recommended for a parser to perform filtering based on time. Instead,
the query utilizing the parser should specify a time range.

FILTERING BY SOURCE TYPE USING A WATCHLIST

At times, the occurrence lacks data that could aid in sorting out particular
source categories.

When Syslog messages are sent from various sources, it can be difficult to
differentiate Infoblox DNS events from others. To overcome this challenge, the
parser refers to the ASimSourceType watchlist, which contains a list of sources
defining the relevant events.

Incorporate the ASimSourceType watchlist into your parsers.

1. To start your parser, add the subsequent line at the beginning.

let Sources_by_SourceType=(sourcetype:string){_GetWatchlist('ASimSourceType') | where SearchKey == tostring(sourcetype) | extend Source=column_ifexists('Source','') | where isnotempty(Source)| distinct Source };

1. Integrate the watchlist into the parser filtering section as a filter. As an
instance, the filtering section of the Infoblox DNS parser has this feature.

| where Computer in (Sources_by_SourceType('InfobloxNIOS'))

To use this sample in your parser:

* Replace Computer with the name of the field that includes the source
information for your source. You can keep this as Computer for any parsers
based on Syslog.

* Replace the InfobloxNIOS token with a value of your choice for your parser.
Inform parser users that they must update the ASimSourceType watchlist using
your selected value, as well as the list of sources that send events of this
type.

FILTERING BASED ON PARSER PARAMETERS

Ensure that your filtering parser accepts the relevant schema's filtering
parameters as specified in the reference article. To guarantee the correct
function signature, it is recommended to use an existing parser as a starting
point. Additionally, filtering code for the same schema is typically similar
across filtering parsers.

When filtering, make sure that you:

* Before parsing, it is recommended to apply a filter using the physical
fields. If the initial filtered results are not precise, it may be necessary
to conduct the test again after parsing in order to optimize the results. For
further details on optimizing the filtering process, refer to the information
on filtering optimization.
* If the parameter retains its default value and is undefined, there is no need
to apply any filters.

The below instances demonstrate the application of filtering for a string
parameter, usually set to the default value of '*', and for a list parameter,
which usually has an empty list as its default value.

srcipaddr=='*' or ClientIP==srcipaddr
array_length(domain_has_any) == 0 or Name has_any (domain_has_any)

FILTERING OPTIMIZATION

To optimize the parser's efficiency, take into consideration these filtering
suggestions:

* To optimize performance, it is recommended to filter using built-in fields
instead of parsed fields. Although it may seem convenient to use parsed
fields for filtering, it can significantly reduce performance.
* Utilize optimized performance operators, specifically == , has , and
startswith . Avoid using operators like contains or matches regex as they can
significantly affect performance.

It can be challenging to adhere to recommendations for optimizing performance
when filtering. For instance, has is not as precise as contains . Similarly, it
may be less accurate to match the built-in field SyslogMessage compared to
comparing an extracted field like DvcAction . In these scenarios, it is
advisable to pre-filter using a performance-boosting operator over a built-in
field and then repeat the filter with more precise conditions after parsing.

Check out the Infoblox DNS parser snippet as an illustration. Initially, the
parser verifies that the SyslogMessage field includes the term designated by has
and client . However, since this term may appear elsewhere in the message, the
parser double-checks it after parsing the Log_Type field to confirm that it is
indeed the value of the field client .

Syslog | where ProcessName == "named" and SyslogMessage has "client"
…
| extend Log_Type = tostring(Parser[1]),
| where Log_Type == "client"

Note

It is unnecessary for parsers to filter by time since the parser's query already
includes time filtering.

PARSING

After the query has identified the appropriate records, parsing may be required,
especially if a single text field conveys multiple event fields.

Below is a list of KQL operators that parse, arranged according to their
optimization for performance. The first operator on the list delivers the most
optimal performance, while the last operator on the list delivers the least
optimal performance.

Operator Description split Analyze a sequence of values that are separated by a
delimiter. parse_csv Convert a line of values formatted with comma-separated
values (CSV) into separate entities. parse Extract numerous values from any
string by applying a pattern that may either be a simplified one for improved
efficiency or a regular expression. extract_all Extracting individual values
from any string can be achieved by implementing a regular expression. If parse
applies a regular expression, its efficiency is comparable to that of
extract_all . extract To obtain a single value from a random string, regular
expressions can be utilized. For better performance, it is recommended to use
extract instead of parse or extract_all . However, using multiple activations of
extract on the same source string is less efficient. It is advisable to avoid
this and instead use a single parse or extract_all . parse_json To extract
specific values from a JSON formatted string, it is recommended to use parse ,
extract , or extract_all instead of parsing the entire string. This approach
offers improved performance and efficiency. parse_xml To extract specific values
from an XML-formatted string, it's recommended to utilize parse , extract , or
extract_all instead of parsing the entire string. This approach offers improved
performance.

Besides string parsing, the parsing phase might necessitate additional
processing of the initial values, such as:

* Formatting and type conversion . The source field, once extracted, may need
to be formatted to fit the target schema field. For example, you may need to
convert a string representing date and time to a datetime field. Functions
such as todatetime and tohex are helpful in these cases.

* Value lookup . The value of the source field, once extracted, may need to be
mapped to the set of values specified for the target schema field. For
example, some sources report numeric DNS response codes, while the schema
mandates the more common text response codes. The functions iff and case can
be helpful to map a few values.

followed by

For example, the Microsoft DNS parser assigns the EventResult field based on
the Event ID and Response Code using an iff statement, as follows:

and then

extend EventResult = iff(EventId==257 and ResponseCode==0 ,'Success','Failure')

, after which

For several values, use datatable and lookup , as demonstrated in the same
DNS parser:

is discussed along with

let RCodeTable = datatable(ResponseCode:int,ResponseCodeName:string) [ 0, 'NOERROR', 1, 'FORMERR'....];
...
| lookup RCodeTable on ResponseCode
| extend EventResultDetails = case (
isnotempty(ResponseCodeName), ResponseCodeName,
ResponseCode between (3841 .. 4095), 'Reserved for Private Use',
'Unassigned')

Note

The conversion process cannot solely rely on lookup because several values are
associated with Reserved for Private Use or Unassigned . Consequently, the query
employs both lookup and case. Nevertheless, utilizing case for all values is
significantly less efficient than the current approach.

MAPPING VALUES

Normalization of the extracted values becomes necessary in various instances.
ASIM, for instance, uses colons to separate MAC addresses while a
hyphen-delimited MAC address may be sent by the source. The extend operator
serves as the primary tool for transforming values and is accompanied by a wide
range of KQL functions for strings, numbers, and dates as showcased in the
Parsing section earlier.

Employ the case , iff , and lookup statements to match a group of values to
those that comply with the target field .

To establish the mapping of source values to target values, utilize the
datatable operator along with lookup to execute the mapping process. An instance
of this would be:

let NetworkProtocolLookup = datatable(Proto:real, NetworkProtocol:string)[
6, 'TCP',
17, 'UDP'
];
let DnsResponseCodeLookup=datatable(DnsResponseCode:int,DnsResponseCodeName:string)[
0,'NOERROR',
1,'FORMERR',
2,'SERVFAIL',
3,'NXDOMAIN',
...
];
...
| lookup DnsResponseCodeLookup on DnsResponseCode
| lookup NetworkProtocolLookup on Proto

It's worth noting that lookup remains beneficial and effective even in cases
where the mapping offers only two likely options.

In cases where the mapping conditions are intricate, opt for either the iff or
case functions. Additionally, the iff function facilitates the mapping of two
values.

| extend EventResult =
iff(EventId==257 and ResponseCode==0,'Success','Failure’)

The case function allows for more than two target values. To combine lookup and
case , an example is provided below. When a lookup value cannot be found, the
previous example returned an empty value in the DnsResponseCodeName field.
However, the following example improves upon this by using the result of the
lookup operation when available, and specifying additional conditions otherwise.

| extend DnsResponseCodeName =
case (
DnsResponseCodeName != "", DnsResponseCodeName,
DnsResponseCode between (3841 .. 4095), 'Reserved for Private Use',
'Unassigned'
)

PREPARE FIELDS IN THE RESULT SET

The fields in the results set must be prepared by the parser to guarantee the
utilization of normalized fields.

The subsequent KQL operators are employed to ready fields within your outcome
collection.

Operator Description When to use in a parser Rename the project. Renames fields.
When there is a need to change the name of a field that already exists in the
event, utilize project-rename . The performance of operations on the renamed
field remains efficient as it still functions as a built-in field.
Unfortunately, there is not enough context to accurately rephrase the phrase
"project-away" without potentially changing its intended meaning. Please provide
more information or context so that I can assist you better. Removes fields. To
exclude certain fields from the result set, use the code project-away . It is
advised to keep the non-normalized original fields in the result set unless
their presence may lead to confusion or cause performance issues due to their
large size. project The statement chooses fields that were either pre-existing
or newly created and eliminates any other fields. Avoid using it in a parser
since other fields that are not normalized should not be removed by the parser.
To remove certain fields like temporary values utilized during parsing, use
project-away . extend Add aliases. The extend operator does not only create
calculated fields but also generates aliases.

HANDLE PARSING VARIANTS

Important

The various variations denote distinct event categories that are often
associated with diverse schemas, resulting in the need for separate parsers.

Often, an event stream includes variants that need distinct parsing logic. To
handle different variants using a single parser, one can either utilize
conditional statements like iff and case , or opt for a union structure.

In order to handle multiple variants using union , it is recommended to create
individual functions for each variant and then merge the outcomes using the
union statement.

let AzureFirewallNetworkRuleLogs = AzureDiagnostics
| where Category == "AzureFirewallNetworkRule"
| where isnotempty(msg_s);
let parseLogs = AzureFirewallNetworkRuleLogs
| where msg_s has_any("TCP", "UDP")
| parse-where
msg_s with networkProtocol:string
" request from " srcIpAddr:string
":" srcPortNumber:int
…
| project-away msg_s;
let parseLogsWithUrls = AzureFirewallNetworkRuleLogs
| where msg_s has_all ("Url:","ThreatIntel:")
| parse-where
msg_s with networkProtocol:string
" request from " srcIpAddr:string
" to " dstIpAddr:string
…
union parseLogs, parseLogsWithUrls…

To prevent redundant events and unnecessary processing, it is important to begin
each function by filtering only the events intended for parsing using native
fields. Additionally, project-away can be utilized at each branch before the
union if necessary.

DEPLOY PARSERS

Manually copying parsers to the Azure Monitor Log page and saving the query as a
function can be a helpful way to deploy parsers, especially if you want to test
them. See the Create a function section for further details.

For the deployment of many parsers, it is suggested to utilize parser ARM
templates in the following manner.

2. Create a YAML file based on the relevant template for each schema and
include your query in it. Start with the YAML template relevant for your
schema and parser type, filtering or parameter-less.

4. Use the ASIM Yaml to ARM template converter to convert your YAML file to an
ARM template.

6. If deploying an update, delete older versions of the functions using the
portal or the function delete PowerShell tool.

8. Deploy your template using the Azure portal or PowerShell.

By utilizing linked templates, it is possible to merge various templates into a
solitary deployment process.

Tip

ARM templates offer the flexibility to deploy various resources, including
parsers, connectors, analytic rules, and watchlists. A parser can refer to a
co-deployed watchlist as an instance.

TEST PARSERS

Here, we will discuss the testing tools offered by ASIM that allow you to test
your parsers. However, it is important to note that parsers are often intricate
pieces of code, and as such, it is recommended to supplement automated testing
with standard quality assurance practices like code reviews.

INSTALL ASIM TESTING TOOLS

Deploy the ASIM testing tool to a Microsoft Sentinel workspace in order to carry
out tests on ASIM.

*
* Your parser is deployed.
* The table from which the parser derives its information can be accessed.
* The parser's source table is filled with a diverse array of pertinent
occurrences.

VALIDATE THE OUTPUT SCHEMA

To verify the validity of the schema produced by your parser, execute the query
provided on the Microsoft Sentinel Logs page, which utilizes the ASIM schema
tester.

| getschema | invoke ASimSchemaTester('')

Handle the results as follows:

Error Action The required [<Field>] is absent. Include the data in your parser.
Often, it will be a calculated or persistent value, rather than a pre-existing
field from the source. The presence of the mandatory column [<Field>]
necessitates the requirement of the missing field [<Field>]. Incorporate the
field into your parser, as it often indicates the category of the corresponding
column. The presence of column [<Field>] makes it mandatory to fill the missing
field [<Field>]. Include the new field in your parser which often indicates the
category of the corresponding column. The mandatory alias for the field is
missing, which is already being used as a column. Add the alias to your parser
An existing column [<Field>] is being aliased instead of using the recommended
alias [<Field>]. Add the alias to your parser The existing column [<Field>] is
being aliased, but the optional alias [<Field>] is missing. Add the alias to
your parser The mandatory alias for the field is missing, resulting in a missing
column with the same field name. Fix the error in the aliased field and include
the respective alias in your parser to resolve this issue. The field [<Field>]
has a type mismatch. Its current type is [<Type>], but it should be of type
[<Type>]. It is important to verify the accuracy of the normalized field by
utilizing a conversion function like tostring .

Info Action The field [<Field>] that is recommended is not present. It may be
beneficial to include this particular field in your parser.

Info Action The specified alias [<Field>] is missing, while the column it is
supposed to alias does not exist. Ensure that the alias is included in the
parser when adding the aliased field. There is an issue with an optional alias
[<Field>], which is trying to alias a column [<Field>] that does not exist.
Ensure that you include the same alias when adding the aliased field to the
parser. An optional field [<Field>] is absent. Although optional fields are
frequently absent, it is advisable to check the inventory to identify which
optional fields can be linked from the source. An additional field that has not
been normalized [<Field>]. It's important to examine the list of unnormalized
fields to identify any values that could potentially be associated with an
optional field, despite their validity.

Note

Content may not work correctly if there are errors in the parser. While warnings
will not hinder the working of content, the results' quality may be compromised.

VALIDATE THE OUTPUT VALUES

Utilize the ASIM data tester in Microsoft Sentinel's Logs page to ensure the
accuracy of the values generated by your parser.

| limit | invoke ASimDataTester('')

To avoid timing out, it is recommended to either set X to the highest feasible
number or adjust the time range picker when running this resource-intensive test
on your data set.

Handle the results as follows:

Message Action The column [<Field>] is facing a type mismatch error. Its current
data type is [<Type>], but it should have been [<Type>]. It is important to
verify that the normalized field is of the appropriate type, which can often be
achieved through the utilization of a conversion function like tostring . An
error has occurred due to the invalid value(s) entered for the field [<Field>]
of type [<Logical Type>], with up to 10 listed. To ensure accurate mapping of
the source field to the output field, it is important that the parser is
configured correctly. Once the mapping is established, it is recommended to
modify the parser to convert the source value to the appropriate type, value or
format. For more details on the correct values and formats for each logical
type, consult the list of logical types. It should be noted that the testing
tool only displays a limited sample of 10 invalid values. Caution: A required
[<Field>] is missing a value. It is important to fill in mandatory fields, and
not simply declare them. Consider populating empty fields using alternative
sources for relevant records. There is a missing value in the recommended field
labeled as [ ]. It is advisable to fill in the suggested fields. If the current
source is blank, verify if the field can be filled from alternate sources for
the respective records. An optional field has an empty value indicated by the
message: "Empty value in [<Field>]". Verify if the field that is aliased is
either compulsory or advisable. Additionally, assess the possibility of sourcing
information for its population from alternative sources.

Numerous messages also include the number of records that triggered them along
with their corresponding percentage in the overall sample. This percentage
serves as a reliable gauge of the issue's significance. Take, for instance, a
suggested field.

* A parsing issue could be suggested by a majority of empty values, around 90%.
* A possible reason for 25% of the values being empty is that there could have
been an event variant which wasn't parsed accurately.
* A few missing values may not be a significant concern.

Note

Mistakes in the parser may hinder the proper functioning of the content, while
alerts may not stop the content from working but may lower the precision of the
results.

CONTRIBUTE PARSERS

Consider submitting the parser to the primary ASIM distribution for inclusion.
If approved, the built-in parsers of ASIM will be accessible to all customers.

To contribute your parsers:

Step Description Develop the parsers - Construct a parser for filtering and
another one without parameters. - Generate a YAML file for the parser as
instructed in the Deploying Parsers section. Test the parsers Ensure that all
tests are successfully passed by your parsers without any errors. In case there
are any remaining warnings, they should be duly documented in the parser y AML
file as per the instructions provided. Contribute To contribute to the Microsoft
Sentinel GitHub repository, you must first create a pull request. Within the PR,
include your parsers YAML files in the ASIM parser folders (
/Parsers/ASim<schema>/Parsers ) and add representative sample data to the sample
data folder ( /Sample Data ).

DOCUMENTING ACCEPTED WARNINGS

In case the ASIM testing tools' warnings are deemed appropriate for a parser,
record them as accepted warnings in the parser YAML file's Exceptions section,
following the example given below.

Exceptions:
- Field: DnsQuery
Warning: Invalid value
Exception: May have values such as "1164-ms-7.1440-9fdc2aab.3b2bd806-978e-11ec-8bb3-aad815b5cd42" which are not valid domains names. Those are are related to TKEY RR requests.
- Field: DnsQuery
Warning: Empty value in mandatory field
Exception: May be empty for requests for root servers and for requests for RR type DNSKEY

The warning mentioned in the YAML file must be a brief version of the warning
message that identifies it uniquely. This value is utilized to match warning
messages during automated testing and exclude them.

NEXT STEPS

The focus of this write-up is on the creation of ASIM parsers.

Learn more about ASIM parsers:

*
* ASIM parsers overview
*
* Use ASIM parsers
*
* Manage ASIM parsers
*
* The ASIM parsers list

Learn more about the ASIM in general:

* Either view the slides or attend the Deep Dive Webinar on Microsoft
Sentinel's Normalizing Parsers and Normalized Content.
* Overview of the Advanced Security Information Model (ASIM).
* Schemas for the Advanced Security Information Model (ASIM).
* Content related to the Advanced Security Information Model (ASIM).

Infoblox and Parsing Questions, Re: Infoblox and Parsing Questions. @majo1
According to my experience with Azure Sentinel, the parsing has to be done at
the Syslog server. …
Tags:
deploy the asim testing tool develop advanced security information model asim
parsers public preview create custom fields in azure monitor

SENTINELS OF THE MULTIVERSE

While I am not really a fan of vengeance mode due to how one dimensional it is
relative to the normal game, this here is the main reason why you should look

AZURE SENTINEL FORTINET PARSER

Have you noticed any unusual behavior with your Fortinet Fortigate events in
Azure Sentinel? Recently, from 6/11-6/12, we have observed issues with the
internal Fortigate Parser. Prior to 6/11, we only detected two distinct device
externalIDs, FG5H0E##########. However, after 6/11, we noticed more than 40
unique externalIDs, including the original two and other misinterpreted IDs. It
is important to note that this data is available on the CommonSecurityLog table,
and we do not parse it before interpreting it.

The following alphanumeric codes are listed: - FG5H0E####### - FG5H0E##### -
FG5H - FG5H0E##########FTNTFGTeven - FG5H0E##########FTNTFGTeventtim

The information is being transmitted to Azure Sentinel using the log forwarder
specified in the knowledge base articles, and I have verified that this function
remains unchanged.

The mentioned IDs seem to be connected with the Fortinet modules as per the
information available on https://www.forticloud.com/help/supportedmodels.html.

As you mentioned, it appears that the problem lies with the fortinet parser.
Note that Microsoft Q&A only caters to Microsoft products, and fortinet has its
own forum for support. I suggest seeking assistance from there at this link:
https://forum.fortinet.com/

Enrich table with entities from security incident, Microsoft Sentinel - SAP
continuous threat monitoring with UEBA entity pages by KobyMymon on November 22,
2021 2911 Views

PARSE TEXT DATA IN AZURE MONITOR LOGS

Azure Monitor gathers log data that may contain several pieces of information in
a single property. To simplify its usage in queries, it is recommended to parse
this data into multiple properties. For instance, custom log is a typical
example of data that collects an entire log entry with multiple values in a
single property. However, creating distinct properties for each value enables
you to search and aggregate on each of them.

In this article, various methods for parsing log data in azure monitor are
discussed. The article compares the benefits of using these methods during data
ingestion and retrieval in a query.

PARSING METHODS

You have the option to parse data during data collection or when analyzing the
data with a query. Both approaches offer distinct benefits, which are outlined
below.

PARSE DATA AT COLLECTION TIME

During collection time, when you use Parse data , you can set up Custom Fields
that will generate additional properties within the table. This way, queries
won't require any parsing logic and can treat these properties just like any
other field in the table.

The benefits of using this approach are as follows:

* The query process becomes simpler as there is no requirement to incorporate
parse commands while querying the accumulated data.
* Improved query performance as parsing is not required during the query
process.

The following are the drawbacks associated with this approach:

* Advance definition is mandatory and redundant data collection must be
avoided.
* Modifying the parsing logic will solely affect fresh data.
* The queries offer more parsing options than what is currently available.
*
* Increases latency time for collecting data.
* Managing errors can pose a challenge.

PARSE DATA AT QUERY TIME

To split data into multiple fields, you can incorporate parsing logic in your
query without altering the original table.

Benefits associated with this approach incorporate the subsequent:

* This is applicable to all forms of data, including those that have already
been gathered.
* All data can be instantly updated with alterations in the logic.
* The parsing options are versatile and include pre-set logic for specific
types of data structures.

The drawbacks of this approach encompass:

* Using functions to emulate a table can help alleviate the need for more
intricate queries.
* It is necessary to duplicate the parsing logic in several queries, but some
of the logic can be consolidated into functions for sharing.
* Performing intricate computations on massive data sets (billions of records)
can lead to excessive computation time and resource consumption.

PARSE DATA AS IT'S COLLECTED

Check out the guide on Azure Monitor's custom field creation to learn how to
parse data during its collection. This process results in custom attributes in
the table that can be utilized in queries similar to any other attributes.

PARSE DATA IN QUERY USING PATTERNS

In Kusto query language, various operators can be utilized to extract a
particular data element into one or more new properties, whenever a repetitive
pattern is present in the data you wish to parse.

SIMPLE TEXT PATTERNS

Incorporate the parse operator in your query to generate custom properties from
a string expression. With this technique, you can determine the pattern to be
recognized and provide names for the generated properties. This approach is
particularly valuable for data containing strings with key-value pairs in the
format of "key=value".

Take into account a personalized log containing information presented in the
subsequent layout.

Time=2018-03-10 01:34:36 Event Code=207 Status=Success Message=Client 05a26a97-272a-4bc9-8f64-269d154b0e39 connected
Time=2018-03-10 01:33:33 Event Code=208 Status=Warning Message=Client ec53d95c-1c88-41ae-8174-92104212de5d disconnected
Time=2018-03-10 01:35:44 Event Code=209 Status=Success Message=Transaction 10d65890-b003-48f8-9cfc-9c74b51189c8 succeeded
Time=2018-03-10 01:38:22 Event Code=302 Status=Error Message=Application could not connect to database
Time=2018-03-10 01:31:34 Event Code=303 Status=Error Message=Application lost connection to database

By adding the "project" line to the query, the data can be parsed into separate
properties. This ensures that only the calculated properties are returned and
not the "RawData" property, which contains the entire entry from the custom log.

MyCustomLog_CL
| parse RawData with * "Time=" EventTime " Event Code=" Code " Status=" Status " Message=" Message
| project EventTime, Code, Status, Message

Here's an additional instance where the UPN username is extracted from the
AzureActivity record.

AzureActivity
| parse Caller with UPNUserPart "@" *
| where UPNUserPart != "" //Remove non UPN callers (apps, SPNs, etc)
| distinct UPNUserPart, Caller

REGULAR EXPRESSIONS

In case your information satisfies a specific pattern, you may employ certain
methods that require Regular Expressions to isolate particular data points. For
instance, the given demonstration applies "extract" function on "AzureActivity"
logs to extract the users with unique "UPN" field.

AzureActivity
| extend UPNUserPart = extract("([a-z.]*)@", 1, Caller)
| distinct UPNUserPart, Caller

Azure Monitor utilizes re2 version of Regular Expressions to facilitate parsing
on a large scale. It should be noted that this variant of regular expression is
comparable but not identical to other variants. For more information, please
refer to the re2 expression syntax.

PARSE DELIMITED DATA IN A QUERY

Data that is delimited uses a common character, like a comma, to separate fields
in a CSV file. By using the split function and specifying a delimiter, you can
parse the delimited data. The extend operator can be used to return all fields
in the data or to select specific fields for the output.

Note

To utilize split results in operators and filters, it may be necessary to
perform explicit data type casting, as split returns a dynamic object.

Take into account a personalized log that contains information in CSV layout.

2018-03-10 01:34:36, 207,Success,Client 05a26a97-272a-4bc9-8f64-269d154b0e39 connected
2018-03-10 01:33:33, 208,Warning,Client ec53d95c-1c88-41ae-8174-92104212de5d disconnected
2018-03-10 01:35:44, 209,Success,Transaction 10d65890-b003-48f8-9cfc-9c74b51189c8 succeeded
2018-03-10 01:38:22, 302,Error,Application could not connect to database
2018-03-10 01:31:34, 303,Error,Application lost connection to database

The given query summarizes the data by two calculated properties by parsing it.
The RawData property is split into a string array in the first line, while the
following lines assign names to each property and utilize functions to convert
them to the correct data type before adding them to the output.

PARSE PREDEFINED STRUCTURES IN A QUERY

In case your data adheres to a recognizable format, you could potentially
leverage the parsing functions available in the Kusto query language to process
these predefined structures.

*
* JSON
*
* XML
*
* IPv4
*
* URL
*
* URL query
*
* File path
*
* User agent
*
* Version string

This query example analyzes the JSON-structured Properties field of the
AzureActivity table. The parsed results are saved in a dynamic property named
parsedProp, which contains the named values of the JSON. These values are
utilized for filtering and summarizing the query results.

AzureActivity
| extend parsedProp = parse_json(Properties)
| where parsedProp.isComplianceCheck == "True"
| summarize count() by ResourceGroup, tostring(parsedProp.tags.businessowner)

It is advisable to use parsing functions only when your query involves multiple
properties from the formatted data as they can be processor intensive. In case
of a simple pattern matching processing, the performance will be faster.

Demonstrated below is an instance that illustrates the disintegration of TGT
Preauth type for a domain controller. This type is solely present in the
EventData field, which is in the form of an XML string, and no other information
from this area is necessary. To extract the necessary information, the parse
method is utilized.

SecurityEvent
| where EventID == 4768
| parse EventData with * 'PreAuthType">' PreAuthType '' *
| summarize count() by PreAuthType

USE FUNCTION TO SIMULATE A TABLE

If you have several inquiries that perform identical parsing of a specific
table, it is recommended to develop a function that yields parsed data instead
of duplicating the parsing logic in each inquiry. Subsequently, you can replace
the initial table with the function alias in other queries.

To utilize the parsed data from the comma-delimited custom log sample in various
queries, one can generate a function by executing the provided query and then
saving it using the alias "MyCustomCSVLog".

Instead of using the actual table name, queries can now utilize the alias
"MyCustomCSVLog".

MyCustomCSVLog
| summarize count() by Status,Code

NEXT STEPS

* Gain knowledge on log queries for scrutinizing the information gathered from
various data sources and solutions.

Modify content to use the Microsoft Sentinel Advanced, Normalized security
content in Microsoft Sentinel includes analytics rules, hunting queries, and
workbooks that work with unifying normalization …
Read other technology post: Xposed: how to block a method call
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