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LUCY MANUAL Applies to LUCY versions above 4.7


Yara Rules

What is Yara?

Yara is a powerful engine that helps to identify malware (mostly).
Below is the short description of the engine by its developers taken from their github:

YARA is a tool aimed at (but not limited to) helping malware researchers to identify and classify malware samples. With YARA you can create descriptions of malware families (or whatever you want to describe) based on textual or binary patterns. Each description, a.k.a rule, consists of a set of strings and a boolean expression which determine its logic.

In the Screener, Yara is used to analyze *.eml files.
In case if the received report is *.msg, it'll be automatically converted into *.eml.

Yara Rules

In the Settings → Yara Rules there is a list of predefined Rules that can be Modified.

Let us review the very first Yara rule as an example.

  • Link and text not equal - The name of the Rule.
  • Enabled\Disabled - Status of the rule. Whether it is active\inactive.
  • Name - New name of the Rule. Applied after a save.
  • Target (Body\Attachement) - What to scan, the body of an *.eml file or attachment in it.
  • Rule Constructor - In the example, it can not display too complex Yara rule.
  • Yara Rule Text - Text of the rule itself.
  • Description - Description that helps a user to understand the purpose of the rule.
  • Score - Amount of score that is going to be assigned to an incident.
  • Category - To what category the rule is applied.

Add a Rule

To add a new rule, please use the +Add button.

The New Yara Rule page allows building a new rule upon user's requirements.

Rule Constructor

The most interesting part of the page is the Rule Constructor. It is separated into Condition Bloks. Each block has:

Field Condition Value or Field
Delivered To Equals To Delivered To
Subject Not Equals To Subject
Date Contains Date
From Not Contains From
To To
Actual Sender Email Actual Sender Email
Visible Sender Email Visible Sender Email
Recipient Email Recipient Email
Origin Server Hostname It can be configured to have any string or digital value

In case if multiple expressions are set in the same block. a "Rule Operator" option will appear.
The created Conditional Block will assign some spam score to an incident only if any of the conditions matched\all of the conditions matched.

Let us review an example below.

The rule will work only if both Conditional Blocks are triggered, since it is set to "All".
Conditional Block #1 will be triggered if any of the conditions are triggered, which are Subject equals to "Ipad" and the email is not from "".
Conditional Block #2 will be triggered if the only condition is true, the email is purposed to a guy with email "".

As the result, the constucture has generated the following Yara rule:

 ( eml.subject == "Ipad" or eml.from != "") and ( == "") 

Useful Example

Let us add a Yara rule that would be pretty useful.
In this example, we will create a dictionary of trusted domain names, then we will use the dictionary to give a negative spam score to an incident.

Step 1: Create the dictionary

Go to Settings → Yara Dictionaries → +Add.

Set the name and add a domain name of your company.
In my case it is MyCompany.dict and the domain name is Set it to be "Enabled" and press the "Save" button.

Step 2: Create the Yara Rule

Go to Settings → Yara Rules → +Add.

Set it to be Enabled, Set the name, and input the Yara Rule below.

 for any i in (["MyCompany.dict"].lines_count) : (fr.lines["MyCompany.dict"].data[i] == eml.get_domain_from_email(eml.actual_sender_email)) 

This means that for (cycle) any line from 0 to maximum of existing lines in the dictionary (lines_count) there should be a comparison, if a line matches with the domain name in the sender email domain (eml.get_domain_from_email(eml.actual_sender_email)))
Where MyCompany.dict is the name of the dictionary.

The important trick, set the score to be a negative value, for example -20.
With this configuration set all of the emails from domains that are in the dictionary will be considered as safe and the negative value will cover the false positives. The category is up to you. It'll be assigned to the incident in the overview section.

Step 3: Review the result

It can be seen that the category is assigned according to the internal rules, including the "safe" one.
Preconfigured Yara rule in step 2 gave the incident -20 scores.

So from now on it is enough to add a domain name into the dictionary we created for it automatically considered as safe.

Deep into Yara inside of Screener

EML Module

We use the "eml" module for the Yara engine. It is designed to process eml files. It can be found in the Yara rule example below:

 eml.get_domain_from_email(eml.headers["message-id"]) != eml.get_domain_from_email(eml.headers["from"]) 

It is possible to build your own complex Yara rules with its models and endpoints available in the table below:

delivered_to string Extracts information from the header "Delivered_to"
message_id string Extracts information from the header "Message_id"
subject string Extracts information from the header "Subject
date string Extracts information from the header "Date"
x_mailer string Extracts information from the header "X_mailer"
from string Extracts information from the header "From"
to string Extracts information from the header "To"
cc string Extracts information from the header "CC"
bcc string Extracts information from the header "bcc
actual_sender_email string Extracts information from the email regarding the True Sender
visible_sender_email string Extracts information about the Visible Sender Email
recipient_email string Extracts information regarding the recipient email address
origin_server_hostname string Extracts information regarding server hostname
headers string_dictionary example: eml.headers["headername"]="custom text"
number_of_domains integer Number of domain names in the email
domains string_array The domain names in the email
number_of_ip_addresses integer Number of IP addresses in the email
ip_addresses string_array The IP addresses in the email
number_of_http_links integer Number of HTTP links in the email
http_links struct (link, text) example: eml.http_links[1].text="domain" and eml.http_links[1].link="". It can be used together or separately
FunctionsReturn TypeExample Description
"capture" string Example: eml.capture("hello (world)", "hey hello world yeah") == "world", Example 2: eml.capture("hello (world)") == "world" Parses a particular text from an email
"get_domain_from_link" String Example: eml.get_domain_from_link("") == "" and eml.get_domain_from_link("") == "" Parse a domain name from a link inside of email
"get_domain_from_email" String Example: eml.get_domain_from_email("") == "" and eml.get_domain_from_email("") == "" Parse domain name from the email
"get_ip" String eml.get_ip("localhost") == "" and eml.get_ip("") == "" The function returns 1 IP from the resolve request
"domain_registration_days" Integer eml.domain_registration_days("") != 0 Gets the amount of days that has passed after the domain registration date

FR Module

We also use fr (file-read) module that is designed to process dictionaries in the Screener.

files_count integer The number of active dictionaries
files string array Names of the active dictionaries
lines struct fr.lines["Dictionary_name.dict"].lines_count == 10 and fr.lines["Dictionary_name_2.dict"].lines_count == 11
Or, it is possible to look for a particular line
fr.lines["Dictionary_name.dict"].data[0] == "value_on_1st_string" and fr.lines["Dictionary_name.dict"].data[9] == "value_on_a_string_9" and fr.lines["Dictionary_name.dict"].data[4] == "value_on_a_string_4"
yara_rules_screener.txt · Last modified: 2021/04/28 10:29 by lucy