DBot Create Phishing Classifier V2

Details

ID	`DBot Create Phishing Classifier V2`
From Version	5.0.0
Tasks	8

README

Creates a phishing classifier using machine learning technique, based on the email content.

Dependencies

This playbook uses the following sub-playbooks, integrations, and scripts.

Sub-playbooks

This playbook does not use any sub-playbooks.

Integrations

This playbook does not use any integrations.

Scripts

GetIncidentsByQuery
Base64ListToFile
DBotPreProcessTextData
DBotTrainTextClassifierV2

Commands

This playbook does not use any commands.

Playbook Inputs

Name	Description	Default Value	Source	Required
modelName	The model name to store in the system.	phishing_model	-	Optional
emailTextKey	The CSV list of incident fields names with the email body or html body. You can also use “	” if you want to choose the first non-empty value from a list of fields.	emailbody	emailbodyhtml	details	-	Optional
emailSubjectKey	The CSV list of incident fields names with the email subject. You can also use “	” if you want to choose the first non-empty value from a list of fields.	emailsubject	name	-	Optional
emailTagKey	The field name with the email tag. Supports a CSV list, the first non-empty value will be taken.	emailclassification	-	Optional
phishingLabels	The CSV list of email tags values and mapping. The script considers only the tags specified in this field. You can map label to another value by using this format: LABEL:MAPPED_LABEL. For example, for 4 values in email tag: malicious, credentials harvesting, inner communitcation, external legit email, unclassified. While training, we want to ignore “unclassified” tag, and refer to “credentials harvesting” as “malicious” too. Also, we want to merge “inner communitcation” and “external legit email” to one tag called “non-malicious”. The input will be: malicious, credentials harvesting:malicious, inner communitcation:non-malicious, external legit email:non-malicious	*	-	Optional
incidentsQuery	The incidents query to fetch the training data for the model.	-	-	Optional
maxIncidentsToFetchOnTraining	The maximum number of incidents to fetch.	3000	-	Optional
hashSeed	If non-empty, hash every word with this seed.	-	-	Optional
historicalDataFileListName	The name of XSOAR list contains historical data training samples for the model.	-	-	Optional
overrideModel	Whether to override the existing model if a model with the same name exists. The default is “false”.	true	-	Optional
incidentTypes	The CSV list of incident types by which to filter.	Phishing	-	Optional
dedupThreshold	Removes emails with similarity greater then this threshold. The range 0-1, where 1 is completly identical.	0.99	-	Optional
removeShortTextThreshold	The sample text of which the total number words are less than or equal to this number will be ignored.	15	-	Optional
modelTargetAccuracy	The model target accuracy, between 0 and 1.	0.8	-	Optional
outputFormat	The output file format. Can be “json” or “pickle”.	pickle	-	Optional

Playbook Outputs

Path	Description	Type
DBotPhishingClassifier.EvaluationScores.Precision.All	The average binary precision over all classes (0-1).	number
DBotPhishingClassifier.EvaluationScores.TP.All	The number of instances of all classes that were predicted correctly.	number
DBotPhishingClassifier.EvaluationScores.Coverage.All	The number of instances that were predicted at a probability greater than the threshold.	number
DBotPhishingClassifier.EvaluationScores.Total.All	The total number of instances.	number
DBotPhishingClassifier.ModelName	The name of the model in XSOAR.	string

Playbook Image

DBot_Create_Phishing_Classifier_V2

Inputs

modelName — The model name to store in the system.
emailTextKey — A comma-separated list of incident fields names with the email body or html body. You can also use "|" if you want to choose the first non-empty value from a list of fields.
emailSubjectKey — A comma-separated list of incident fields names with the email subject. You can also use "|" if you want to choose the first non-empty value from a list of fields.
emailTagKey — The field name with the email tag. Supports a comma-separated list. The first non-empty value will be taken.
phishingLabels — A comma-separated list of email tags values and mapping. The script considers only the tags specified in this field. You can map the label to another value by using this format: LABEL:MAPPED_LABEL. For example, for 4 values in an email tag: malicious, credentials harvesting, inner communication, external legit email, unclassified. While training, we want to ignore the "unclassified" tag, and refer to "credentials harvesting" as "malicious" too. Also, we want to merge "inner communication" and "external legit email" to a single tag called "non-malicious". The input will be: malicious, credentials harvesting:malicious, inner communication:non-malicious, external legit email:non-malicious.
incidentsQuery — The incidents query to fetch the training data for the model.
maxIncidentsToFetchOnTraining — The maximum number of incidents to fetch.
hashSeed — If non-empty, hash every word with this seed.
historicalDataFileListName — The name of the Cortex XSOAR list that contains historical data training samples for the model.
overrideModel — Whether to override the existing model if a model with the same name exists. Default is "false".
incidentTypes — A common-separated list of incident types by which to filter.
dedupThreshold — Remove emails with similarity greater than this threshold, range 0-1, where 1 is completely identical.
removeShortTextThreshold — Sample text of which the total number words that are less than or equal to this number will be ignored.
modelTargetAccuracy — The model target accuracy, between 0 and 1.
outputFormat — The output file format. Can be "json" or "pickle".

Outputs

DBotPhishingClassifier.EvaluationScores.Precision.All — Average binary precision over all classes (0-1).
DBotPhishingClassifier.EvaluationScores.TP.All — The number of instances of all classes that were predicted correctly.
DBotPhishingClassifier.EvaluationScores.Coverage.All — The number of instances that were predicted at a probability greater than the threshold.
DBotPhishingClassifier.EvaluationScores.Total.All — The total number of instances.
DBotPhishingClassifier.ModelName — The name of the model in Cortex XSOAR.

Flowchart

Source

playbook-DBot_Create_Phishing_Classifier_V2.yml playbook-DBot_Create_Phishing_Classifier_V2_From_File.yml playbook-DBot_Create_Phishing_Classifier_V2_Job.yml playbook-DBot_Create_Phishing_Classifier_V2_Job_README.md

Download History Raw

id: DBot Create Phishing Classifier V2 Job
version: -1
name: DBot Create Phishing Classifier V2 Job
description: 'Train the phishing machine learning model. This playbook should be used as job, to run repeatedly, for example every week. '
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inputs: []
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fromversion: "5.0.0"
tests:
- Create Phishing Classifier V2 ML Test
marketplaces:
  - xsoar