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.

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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), 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.

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The button in the Global Filter changes to UPDATE when you need to apply a new value

If it says UPDATE, that means you changed a value but have not yet applied it. Apply the filter by clicking the UPDATE button, or pressing Enter in the NQL text box.

NQL Syntax

NQL Examples

Use the NQL Presets to see additional examples.

For additional examples, see NQL Examples.

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 == 10.0.0.1 is valid, while srcip==10.0.0.1 is not valid.
• Whitespace is not optional in NQL. For example, input==1 is invalid, but input == 1 is valid since the NQL parser will not parse the first example if the whitespace is missing.
• NQL accepts nested parentheses. For example, ((this) || (that)) && other is 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 || C will 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, 10.0.0.0/24 will match 10.0.0.1

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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, 192.168.0.0/24 defines a network with addresses ranging from 192.168.0.0 to 192.168.0.255. CIDR is commonly used in routing, firewall rules, and network segmentation to precisely define IP address ranges.

Operators

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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

Comparison Operators

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.

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NQL fields supporting pattern matching

Pattern matching is only supported for the fields listed below.

Category	Fields
Flow	dstiprep.categories srciprep.categories tags
DNS	answers.rdata query.domain query.host query.name query.publicsuffix
Events	ipinfo.iprep.categories summary tags
Audit	description

Wildcards

Wildcards use special characters to match patterns of text.

Wildcards: Operator Syntax and Meaning

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

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:

Regex: Choice and Grouping

Choice and grouping operators allow you to match one or more alternatives or group expressions:

Regex: Repetition (Greedy and Non-Greedy)

Repetition operators match a specified number of occurrences:

Regex: Character Classes

Character classes match specific sets or ranges of characters:

Regex: Numeric Ranges

Numeric ranges match numbers within a specific range, providing greater flexibility for queries involving numeric values.

Numeric range examples

query.name =~ /ip-(<0-255>-?){3}(<100-200>)\..*/
Matches ip-<0-255>.<100-200>.x.x.

query.name =~ foo<1-100>
Matches "foo1", "foo2", ..., "foo100".

Numeric range performance

Using numeric range matching can simplify queries for numerical intervals but may increase execution time if used with large ranges or complex regex patterns. As with other regex patterns, avoid starting expressions with * or ? when combining them with numeric ranges to prevent performance degradation.

Regex: Special Characters

Special characters perform specific functions within regex patterns:

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.

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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