NQL Overview and Syntax
The Netography Query Language Explained
Refer to the NQL Quick Reference Guide for all the NQL syntax in one table.
Overview
The Netography Query Language (NQL) is the basis for accomplishing many tasks in Fusion.
- Searching for flows, DNS, events, audits, or block records
- Filtering statistics and aggregations
- Defining custom Detection Models to create an event
Becoming familiar with NQL will help you take full advantage of the Fusion platform.
Comparing NQL to Other Query Languages
NQL is designed specifically for network traffic and security analytics, with features optimized for querying this type of data. Unlike SQL, which is a general-purpose query language for relational databases, NQL is tailored to operate on high-throughput network data with a focus on real-time analysis. Similar to Splunk's SPL (Search Processing Language) and Elasticsearch's Query DSL, NQL provides specialized operators and pattern matching capabilities, making it well-suited for dynamic network environments. However, NQL's syntax and rules are distinct, reflecting its purpose-built nature for network security operations.
Using NQL in Fusion
In the Fusion Portal, NQL can be used In the Global Filter (the bar at the top of the Portal), on the Search pages, in Detection Models, or to define widgets inside custom dashboards. It can be used in the API as the value for the search parameter if it exists.
Applying NQL
Results on each page are filtered by the Global Filter, the combination of the date/time range you select and the NQL filter you enter. NQL can also be used on the Search page (in the Investigate section of the left-hand menu bar).
The circle with the inside it in the Global Filter changes from gray to blue when you need to apply a new value
Blue means you changed a value but have not yet applied it. Apply the filter by clicking the return arrow ( ) icon, or pressing Enter in the NQL text box.
Using NQL Presets
If you click the floppy disk ( ), it brings up a list of NQL queries divided into these sections:
- Recent Queries (Flow only), My Presets, Company Presets, System Presets
Search for a preset
The empty text box that appears above the preset list after clicking the floppy disk is a search for presets - start typing in that box to filter the list of presets to find the one you are looking for quickly.
Save NQL to My Presets
Save the NQL from the global filter to My Presets by clicking the floppy disk , selecting the + Save Current NQL row, and then giving it a name (or you can leave it Untitled).
Duplicate, Edit, and Delete Presets
Hover over a preset, and 3 icons will appear on the right-hand side of the preset row. These icons allow you to edit, duplicate, or delete a preset. Only the icons you have permission to perform the actions of will appear (eg you will not see the delete icon on a System Preset).
Search using NQL Intersections
The Search page in the Investigate section of the left-hand menu enables you to perform advanced searches using NQL Intersections. An NQL Intersection allows for the logical combination of multiple independent NQL queries, focusing on the commonality between their results based on specified fields. This approach differs fundamentally from writing a single NQL query that combines conditions using logical operators like AND.
Intersections are a powerful capability for:
Complex Filtering: When you need to filter data across multiple dimensions that don’t necessarily fit into a single query string
Correlation of Data: When you want to correlate different aspects of data, such as matching IPs across different conditions (e.g., ports, protocols) without enforcing all conditions simultaneously.
NQL Syntax
NQL Examples
Use the system presets by clicking to see example NQL.
| Description | NQL |
|---|---|
| IP Reputation Matches | srciprep.count > 0 OR dstiprep.count > 0 |
| Only Privileged Ports | dstport < 1024 |
| Not Broadcast IPs | dstip != 255.255.255.0/24 |
| TCP Ports Scan | tcpflags.syn == true and tcpflags.ack == true and srcport > 1024 |
For additional examples, see NQL Examples, or watch the Video walkthroughs NQL video.
NQL Basics
Using the Netography Query Language is like writing a programming conditional inside an if ( ) statement. The statement should be constructed logically, from left to right, comparing fields to values.
Rules and Limitations
Spacing and Parentheses
- Operators must be surrounded by spaces, e.g.
srcip == 10.0.0.1is valid, whilesrcip==10.0.0.1is not valid. - Whitespace is not optional in NQL. For example,
input==1is invalid, butinput == 1is valid since the NQL parser will not parse the first example if the whitespace is missing. - NQL accepts nested parentheses. For example,
((this) || (that)) && otheris a valid NQL statement. However, this can be considered hard to read, so it is suggested to reduce the use of parentheses when possible, e.g.(this || that) && other - Logic must be unambiguous. e.g.
A && B || Cwill fail. Use parentheses()to prevent ambiguity
Comparisons
- Only integer fields can use numerical comparisons:
< <= > >=
CIDR Notation
- IP fields can be searched with CIDR notation if desired. For example,
10.0.0.0/24will match10.0.0.1
CIDR (Classless Inter-Domain Routing)
CIDR is a method for allocating IP addresses and routing Internet Protocol packets. CIDR uses a suffix (e.g.,
/24) to specify the exact number of bits that represent the network portion of the address. For example,192.168.0.0/24defines a network with addresses ranging from192.168.0.0to192.168.0.255. CIDR is commonly used in routing, firewall rules, and network segmentation to precisely define IP address ranges.
Operators
What is an operator?
In the context of NQL, operators are used to define how fields should be compared or matched against values, allowing users to filter, search, and analyze data according to specific criteria.
Boolean Operators
| Boolean | Description | Example |
|---|---|---|
&& AND | logical AND | this && that |
OR | logical OR | this OR that |
! | NOT must precede expressions in parenthesis | !(srcip == 10.0.0.1) |
Comparison Operators
| Comparison | Description |
|---|---|
== | equals |
!= | not equals |
<= | less than or equals to |
<= | less than |
>= | greater than or equals to |
> | greater tha |
For additional examples, see NQL Examples, or watch the Video walkthroughs NQL video.
Pattern Matching (Wildcards, RegEx, Fuzzy) in NQL
In NQL, pattern matching is performed using regular expressions, wildcards, and fuzzy matching. The operators =~ and !~ specify these matches and their negative counterparts.
NQL fields supporting pattern matching
Pattern matching is only supported for the fields listed below.
Category Fields Flow dstiprep.categories srciprep.categories tagsDNS answers.rdata query.domain query.host query.name query.publicsuffixEvents ipinfo.iprep.categories summary tagsAudit description
Wildcards
Wildcards use special characters to match patterns of text.
Wildcards: Operator Syntax and Meaning
| Operator | Syntax | Meaning | Example Field |
|---|---|---|---|
=~ | *at | Matches zero or more characters. | query.name =~ *at |
!~ | *at | Negative match for zero or more characters. | query.name !~ *at |
=~ | ?at | Matches any single character before "at". | query.name =~ ?at |
!~ | ?at | Negative match for any single character before "at". | query.name !~ ?at |
Note: Avoid beginning patterns with * or ? as this can lead to performance issues due to increased iterations.
Regular Expressions (regex)
Regular expressions (regex) allow for flexible and complex search patterns. In NQL, you can use regex to match patterns with precise rules.
Note: Avoid beginning patterns with * or ? as this can lead to performance issues due to increased iterations.
Regex: Examples
query.domain =~ net_
query.domain =~ netog.i?
query.domain =~ net~
query.domain =~ /[a-z]_/
query.domain =~ /[a-z]\_/ && query.name == neto
Regex: Operator Syntax and Meaning
| Operator | Syntax | Meaning | Example Field |
|---|---|---|---|
=~ | /.*at/ | Matches zero or more characters. | query.name =~ /net.*\.com/ |
!~ | /.*at/ | Negative match for zero or more characters. | query.name !~ /net.*\.com/ |
Regex: Text Boundary Anchors
Boundary anchors specify positions in the text. The start and end characters are implicitly applied to each search and cannot be modified:
| Regexp | Meaning | Description | Example |
|---|---|---|---|
^ | Start of a line or string | Matches the start of a string | ^cat matches catdog |
$ | End of a line or string | Matches the end of a string | cat$ matches dogcat |
Regex: Choice and Grouping
Choice and grouping operators allow you to match one or more alternatives or group expressions:
| Regexp | Meaning | Description | Example |
|---|---|---|---|
xy | x followed by y | Matches "xy" | abc\ matches "abc". |
x OR y | x or y | Matches "x" or "y" | ye` matches "axe" or "aye" |
abc(def)? | Grouping | Matches "abc" or "abcdef" | abc(def)? matches abc and abcdef but not abcd |
Regex: Repetition (Greedy and Non-Greedy)
Repetition operators match a specified number of occurrences:
| Regexp | Meaning | Description |
|---|---|---|
x* | Zero or more occurrences of x | Matches zero or more of "x". Example: a* matches "", "a", "aa". |
x+ | One or more occurrences of x | Matches one or more of "x". Example: a+ matches "a", "aa". |
x? | Zero or one occurrence of x | Matches zero or one of "x". Example: a? matches "" or "a". |
x{n,m} | Between n and m occurrences of x | Matches between n and m of "x". Example: a{2,4} matches "aa", "aaa", "aaaa". |
x{n,} | n or more occurrences of x | Matches n or more of "x". Example: a{2,} matches "aa", "aaa". |
x{n} | Exactly n occurrences of x | Matches exactly n of "x". Example: a{3} matches "aaa". |
Regex: Character Classes
Character classes match specific sets or ranges of characters:
| Regexp | Meaning | Description |
|---|---|---|
. | Matches any single character | Matches any character except a newline. Example: c.t matches "cat" and "cot". |
[abc] | Matches any single character in the set | Matches one of "a", "b", or "c". Example: [aeiou] matches any vowel. |
[^abc] | Matches any single character not in the set | Matches any character except "a", "b", or "c". Example: [^aeiou] matches consonants. |
[a-z] | Matches any single character in the range | Matches any character between "a" and "z". Example: [a-z] matches any lowercase letter. |
Regex: Special Characters
Special characters perform specific functions within regex patterns:
| Regexp | Meaning | Description |
|---|---|---|
\ | Escape character | Escapes special characters to be treated as literals. Example: \. matches a literal period. |
Regex: Reserved Characters
The following characters are reserved as operators and need to be escaped: . ? + * | { } [ ] ( ) " \
Fuzzy Matching
Fuzzy matching accounts for variations in spelling by calculating the Levenshtein distance between terms.
What is the Levenshtein Distance?
The Levenshtein distance is a metric for measuring the difference between two strings. It calculates the minimum number of single-character edits (insertions, deletions, or substitutions) required to change one string into another. For example, the Levenshtein distance between "kitten" and "sitting" is 3, because it requires three edits: replacing 'k' with 's', replacing 'e' with 'i', and adding 'g'. This metric is useful for fuzzy matching, where you want to find matches that are close, but not exact.
Fuzzy Matching: Operator Syntax and Meaning
| Operator | Syntax | Meaning | Example Field |
|---|---|---|---|
=~ | cat~ | Matches terms with an automatically calculated distance. | query.name =~ cat~ |
!~ | cat~ | Negative fuzzy match with automatic distance. | query.name !~ cat~ |
=~ | cat~2 | Matches terms with a maximum distance of 2 changes. | query.name =~ cat~2 |
!~ | cat~2 | Negative match with a maximum distance of 2 changes. | query.name !~ cat~2 |
Updated 17 days ago