Skip to main content

DBotPredictIncidentsBatch

This Script is part of the Machine Learning Pack.#

Apply a trained ML model on multiple incidents at once, to compare incidents how the incidents were labeled by analysts, to the predictions of the model. This script is aimed to help evaluate a trained model using past incidents.

Script Data#


NameDescription
Script Typepython3
Tagsphishing, ml
Cortex XSOAR Version5.0.0

Dependencies#


This script uses the following commands and scripts.

  • GetIncidentsByQuery
  • DBotPredictPhishingWords

Used In#


This script is used in the following playbooks and scripts.

  • VerifyOOBV2Predictions-Test

Inputs#


Argument NameDescription
queryAdditional text by which to query incidents.
incidentTypesA comma-separated list of incident types by which to filter.
fromDateThe start date by which to filter incidents. Date format will be the same as in the incidents query page (valid strings exaple: "3 days ago", ""2019-01-01T00:00:00 +0200")
toDateThe end date by which to filter incidents. Date format will be the same as in the incidents query page (valid strings exaple: "3 days ago", ""2019-01-01T00:00:00 +0200")
limitThe maximum number of incidents to fetch.
tagFieldThe field name with the label. Supports a comma-separated list, the first non-empty value will be taken.
hashSeedIf non-empty, hash every word with this seed.
phishingLabelsA comma-separated list of email tags values and mapping. The script considers only the tags specified in this field. You can map a 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
modelNameThe model name to store in the system.
emailsubjectIncident field name with the email subject.
emailbodyIncident field name with the email body (text).
emailbodyhtmlIncident field name with the email body (html).
populateFieldsA comma-separated list of fields in the object to poplulate.

Outputs#


There are no outputs for this script.