Liblognorm 1.1.2 documentation

How to configure

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How to configure

To use liblognorm, you need 3 things.

  1. An installed and working copy of liblognorm. The installation process has been discussed in the chapter How to install.
  2. Log files.
  3. A rulebase, which is heart of liblognorm configuration.

Log files

A log file is a text file, which typically holds many lines. Each line is a log message. These are usually a bit strange to read, thus to analyze. This mostly happens, if you have a lot of different devices, that are all creating log messages in a different format.


The rulebase holds all the schemes for your logs. It basically consists of many lines that reflect the structure of your log messages. When the normalization process is started, a parse-tree will be generated from the rulebase and put into the memory. This will then be used to parse the log messages.

Each line in rulebase file is evaluated separately.

Rulebase Versions

This documentation is for liblognorm version 2 and above. Version 2 is a complete rewrite of liblognorm which offers many enhanced features but is incompatible to some pre-v2 rulebase commands. For details, see compatiblity document.

Note that liblognorm v2 contains a full copy of the v1 engine. As such it is fully compatible to old rulebases. In order to use the new v2 engine, you need to explicitely opt in. To do so, you need to add the line:


to the top of your rulebase file. Currently, it is very important that

  • the line is given exactly as above
  • no whitespace within the sequence is permitted (e.g. “version = 2” is invalid)
  • no whitepace or comment after the “2” is permitted (e.g. “version=2 # comment”) is invalid
  • this line must be the very first line of the file; this also means there must not be any comment or empty lines in front of it

Only if the version indicator is properly detected, the v2 engine is used. Otherwise, the v1 engine is used. So if you use v2 features but got the version line wrong, you’ll end up with error messages from the v1 engine.

The v2 engine understands almost all v1 parsers, and most importantly all that are typically used. It does not understand these parsers:

  • tokenized
  • recursive
  • descent
  • regex
  • interpret
  • suffixed
  • named_suffixed

The recursive and descent parsers should be replaced by user-defined types in. The tokenized parsers should be replaced by repeat. For the others, currently there exists no replacement, but will the exception of regex, will be added based on demand. If you think regex support is urgently needed, please read our related issue on github, where you can also cast you ballot in favor of it. If you need any of these parsers, you need to use the v1 engine. That of course means you cannot use the v2 enhancements, so converting as much as possible makes sense.


To keep your rulebase tidy, you can use commentaries. Start a commentary with “#” like in many other configurations. It should look like this:

# The following prefix and rules are for firewall logs

Note that the comment character MUST be in the first column of the line.

Empty lines are just skipped, they can be inserted for readability.

User-Defined Types

If the line starts with type=, then it contains a user-defined type. You can use a user-defined type wherever you use a built-in type; they are equivalent. That also means you can use user-defined types in the definition of other user-defined types (they can be used recursively). The only restriction is that you must define a type before you can use it.

This line has following format:

type=<typename>:<match description>

Everything before the colon is treated as the type name. User-defined types must always start with “@”. So “@mytype” is a valid name, whereas “mytype” is invalid and will lead to an error.

After the colon, a match description should be given. It is exactly the same like the one given in rule lines (see below).

A generic IP address type could look as follows:


This creates a type “@IPaddr”, which consists of either an IPv4 or IPv6 address. Note how we use two different lines to create an alternative representation. This is how things generally work with types: you can use as many “type” lines for a single type as you need to define your object. Note that pure alternatives could also be defined via the “alternative” parser - which option to choose is left to the user. They are equivalent. The ability to use multiple type lines for definition, however, brings more power than just to define alternatives.


Especially with user-defined types includes come handy. With an include, you can include definitions already made elsewhere into the current rule set (just like the “include” directive works in many programming languages). An include is done by a line starting with include= where the rest of the line is the actual file name, just like in this example:


The definition is included right at the position where it occurs. Processing of the original file is continued when the included file has been fully processed. Includes can be nested.

To facilitate repositories of common rules, liblognorm honors the


environment variable. If it is set liblognorm tries to locate the file inside the path pointed to by LIBLOGNORM_RULEBASES in the following case:

  • the provided file cannot be found
  • the provided file name is not an absolute path (does not start with “/”)

So assuming we have:

export LIBLOGNORM_RULEBASES=/var/lib/loblognorm

The above example can be re-written as follows:


Note, however, that if stdtypes.rb exist in the current working directory, that file will be loaded insted of the one from /var/lib/liblognorm.

This use facilitates building a library of standard type definitions. Note the the liblognorm project also ships type definitions for common scenarios.


If the line starts with rule=, then it contains a rule. This line has following format:

rule=[<tag1>[,<tag2>...]]:<match description>

Everything before a colon is treated as comma-separated list of tags, which will be attached to a match. After the colon, match description should be given. It consists of string literals and field selectors. String literals should match exactly, whereas field selectors may match variable parts of a message.

A rule could look like this (in legacy format):

rule=:%date:date-rfc3164% %host:word% %tag:char-to:\x3a%: no longer listening on %ip:ipv4%#%port:number%'

This excerpt is a common rule. A rule always contains several different “parts”/properties and reflects the structure of the message you want to normalize (e.g. Host, IP, Source, Syslogtag...).


Literal is just a sequence of characters, which must match exactly. Percent sign characters must be escaped to prevent them from starting a field accidentally. Replace each “%” with “\x25” or “%%”, when it occurs in a string literal.


There are different formats for field specification:

  • legacy format
  • condensed format
  • full json format

Legacy Format

Legay format is exactly identical to the v1 engine. This permits you to use existing v1 rulebases without any modification with the v2 engine, except for adding the version=2 header line to the top of the file. Remember: some v1 types are not supported - if you are among the few who use them, you need to do some manual conversion. For almost all users, manual conversion should not be necessary.

Legacy format is not documented here. If you want to use it, see the v1 documentation.

Condensed Format

The goal of this format is to be as brief as possible, permitting you an as-clear-as-possible view of your rule. It is very similar to legacy format and recommended to be used for simple types which do not need any parser parameters.

Its structure is as follows:

%<field name>:<field type>[{<parameters>}]%

field name -> that name can be selected freely. It should be a description of what kind of information the field is holding, e.g. SRC is the field contains the source IP address of the message. These names should also be chosen carefully, since the field name can be used in every rule and therefore should fit for the same kind of information in different rules.

Some special field names exist:

  • dash (“-”): this field is matched but not saved
  • dot (”.”): this is useful if a parser returns a set of fields. Usually, it does so by creating a json subtree. If the field is named ”.”, then no subtree is created but instead the subfields are moved into the main hierarchy.
  • two dots (”..”): similiar to ”.”, but can be used at the lower level to denote that a field is to be included with the name given by the upper-level object. Note that ”..” is only acted on if a subelement contains a single field. The reason is that if there were more, we could not assign all of them to the single name given by the upper-level-object. The prime use case for this special name is in user-defined types that parse only a single value. Without ”..”, they would always become a JSON subtree, which seems unnatural and is different from built-in types. So it is suggested to name such fields as ”..”, which means that the user can assign a name of his liking, just like in the case of built-in parsers.

field type -> selects the accordant parser, which are described below.

Special characters that need to be escaped when used inside a field description are “%” and ”:”. It is strongly recommended not to use them.

parameters -> This is an optional set of parameters, given in pure JSON format. Parameters can be generic (e.g. “priority”) or specific to a parser (e.g. “extradata”). Generic parameters are described below in their own section, parser-specific ones in the relevant type documentation.

As an example, the “char-to” parser accepts a parameter named “extradata” which describes up to which character it shall match (the name “extradata” stems back to the legacy v1 system):


Whitespace, including LF, is permitted inside a field definition after the opening precent sign and before the closing one. This can be used to make complex rules more readable. So the example rule from the overview section above could be rewritten as:

      % %
      % %
      %: no longer listening on %

When doing this, note well that whitespace IS important inside the literal text. So e.g. in the second example line above “% %” we require a single SP as literal text. Note that any combination of your liking is valid, so it could also be written as:

rule=:%date:date-rfc3164% %host:word% % tag:char-to{"extradata":":"}
      %: no longer listening on %  ip:ipv4  %#%  port:number  %'

To prevent a typical user error, continuation lines are not permitted to start with rule=. There are some obscure cases where this could be a valid rule, and it can be re-formatted in that case. Moreoften, this is the result of a missing percent sign, as in this sample:

rule=:test%field:word ... missing percent sign ...

If we would permit rule= at start of continuation line, these kinds of problems would be very hard to detect.

Full JSON Format

This format is best for complex definitions or if there are many parser parameters.

Its structure is as follows:


Where JSON is the configuration expressed in JSON. To get you started, let’s rewrite above sample in pure JSON form:

rule=:%[ {"type":"date-rfc3164", "name":"date"},
         {"type":"literal", "text:" "},
         {"type":"char-to", "name":"host", "extradata":":"},
         {"type":"literal", "text:": no longer listening on "},
         {"type":"ipv4", "name":"ip"},
         {"type":"literal", "text:"#"},
         {"type":"number", "name":"port"}

A couple of things to note:

  • we express everything in this example in a single parser definition
  • this is done by using a JSON array; whenever an array is used, multiple parsers can be specified. They are exectued one after the other in given order.
  • literal text is matched here via explicit parser call; as specified below, this is recommended only for specific use cases with the current version of liblognorm
  • parser parameters (both generic and parser-specific ones) are given on the main JSON level
  • the literal text shall not be stored inside an output variable; for this reason no name attribute is given (we could also have used "name":"-" which achives the same effect but is more verbose).

With the literal parser calls replaced by actual literals, the sample looks like this:

rule=:%{"type":"date-rfc3164", "name":"date"}
      % %
       {"type":"char-to", "name":"host", "extradata":":"}
      % no longer listening on %
        {"type":"ipv4", "name":"ip"}
        {"type":"number", "name":"port"}

Which format you use and how you exactly use it is up to you.

Some guidelines:

  • using the “literal” parser in JSON should be avoided currently; the experimental version does have some rough edges where conflicts in literal processing will not be properly handled. This should not be an issue in “closed environments”, like “repeat”, where no such conflict can occur.
  • otherwise, JSON is perfect for very complex things (like nesting of parsers - it is not suggested to use any other format for these kinds of things.
  • if a field needs to be matched but the result of that match is not needed, omit the “name” attribute; specifically avoid using the more verbose "name":"-".
  • it is a good idea to start each defintion with "type":"..." as this provides a good quick overview over what is being defined.

Mandatory Parameters


The field type, selects the parser to use. See “fields” below for description.

Optional Generic Parameters


The field name to use. If “-” is used, the field is matched, but not stored. In this case, you can simply not specify a field name, which is the preferred way of doing this.


The priority to assign to this parser. Priorities are numerical values in the range from 0 (highest) to 65535 (lowest). If multiple parsers could match at a given character position of a log line, parsers are tried in priority order. Different priorities can lead to different parsing. For example, if the greedy “rest” type is assigned priority 0, and no other parser is assigned the same priority, no other parser will ever match (because “rest” is very greedy and always matches the rest of the message).

Note that liblognorm internally has a parser-specific priority, which is selected by the program developer based on the specificallity of a type. If the user assigns equal priorities, parsers are executed based on the parser-specific priority.

The default priority value is 30,000.

Field types

We have legacy and regular field types. Pre-v2, we did not have user-defined types. As such, there was a relatively large number of parsers that handled very similar cases, for example for strings. These parsers still work and may even provide best performance in extreme cases. In v2, we focus on fewer, but more generic parsers, which are then tailored via parameters.

There is nothing bad about using legacy parsers and there is no plan to outphase them at any time in the future. We just wanted to let you know, especially if you wonder about some “wereid” parsers. In v1, parsers could have only a single paramter, which was called “extradata” at that time. This is why some of the legacy parsers require or support a parameter named “extradata” and do not use a better name for it (internally, the legacy format creates a v2 parser defintion with “extradata” being populated from the legacy “extradata” part of the configuration).


One or more decimal digits.


A floating-pt number represented in non-scientific form.


A hexadecimal number as seen by this parser begins with the string “0x”, is followed by 1 or more hex digits and is terminated by white space. Any interleaving non-hex digits will cause non-detection. The rules are strict to avoid false positives.


Parses a linux kernel timestamp, which has the format:


where “d” is a decimal digit. The part before the period has to have at least 5 digits as per kernel code. There is no upper limit per se inside the kernel, but liblognorm does not accept more than 12 digits, which seems more than sufficient (we may reduce the max count if misdetections occur). The part after the period has to have exactly 6 digits.


This parses all whitespace until the first non-whitespace character is found. This is primarily a tool to skip to the next “word” if the exact number of whitspace characters (and type of whitespace) is not known. The current parsing position MUST be on a whitspace, else the parser does not match.

Remeber that to just parse but not preserve the field contents, the dash (“-”) is used as field name in compact format or the “name” parameter is simply omitted in JSON format. This is almost always expected with the whitespace type.


This is a highly customizable parser that can be used to extract many types of strings. It is meant to be used for most cases. It is suggested that specific string types are created as user-defined types using this parser.

This parser supports:

  • various quoting modes for strings
  • escape character processing



Specifies how the string is quoted. Possible modes:

  • none - no quoting is permitted
  • required - quotes must be present
  • auto - quotes are permitted, but not required

Not!>!/ parameters.

Default is auto.


Specifies how quote character escaping is handled. Possible modes:

  • none - there are no escapes, quote characters are not permitted in value
  • double - the ending quote character is duplicated to indicate a single quote without termination of the value (e.g. "")
  • backslash - a backslash is prepended to the quote character (e.g \")
  • both - both double and backslash escaping can happen and are supported

Note that turning on backslash mode (or both) has the side-effect that backslash escaping is enabled in general. This usually is what you want if this option is selected (e.g. otherwise you could no longer represent backslash).


Sets the begin quote character.

Default is ”.


Sets the end quote character.

Default is ”.

Note that setting the begin and end quote character permits you to support more quoting modes. For example, brackets and braces are used by some software for quoting. To handle such string, you can for example use a configuration like this:

rule=:a %f:string{"quoting.char.begin":"[", "quoting.char.end":"]"}% b

which matches strings like this:

a [test test2] b

This allows to specify a set of characters permitted in the to-be-parsed field. It is primarily a utility to extract things like programming-language like names (e.g. consisting of letters, digits and a set of special characters only), alphanumeric or alphabetic strings.

If this parameter is not specified, all characters are permitted. If it is specified, only the configured characters are permitted.

Note that this option reliably only works on US-ASCII data. Multi-byte character encodings may lead to strange results.

There are two ways to specify permitted characters. The simple one is to specify them directly for the parameter:


This only supports literal characters and all must be given as a single parameter. For more advanced use cases, an array of permitted characters can be provided:


Here, class is a specify for the usual character classes, with support for:

  • digit
  • hexdigit
  • alpha
  • alnum

In contrast, chars permits to specify literal characters. Both class as well as chars may be specified multiple times inside the array. For example, the alnum class could also be permitted as follows:



One or more characters, up to the next space (\x20), or up to end of line.


One or more characters, up to the next string given in “extradata”.


One or more alphabetic characters, up to the next whitspace, punctuation, decimal digit or control character.


One or more characters, up to the next character(s) given in extradata.



This is a mandatory parameter. It contains one or more characters, each of which terminates the match.


Zero or more characters, up to the next character(s) given in extradata.



This is a mandatory parameter. It contains one or more characters, each of which terminates the match.


Zero or more characters untill end of line. Must always be at end of the rule, even though this condition is currently not checked. In any case, any definitions after rest are ignored.

Note that the rest syntax should be avoided because it generates a very broad match. If it needs to be used, the user shall assign it the lowest priority among his parser definitions. Note that the parser-sepcific priority is also lowest, so by default it will only match if nothing else matches.


Zero or more characters, surrounded by double quote marks. Quote marks are stripped from the match.


Zero or more characters, possibly surrounded by double quote marks. If the first character is a quote mark, operates like quoted-string. Otherwise, operates like “word” Quote marks are stripped from the match.


Date in ISO format (‘YYYY-MM-DD’).


Time of format ‘HH:MM:SS’, where HH is 00..23.


Time of format ‘HH:MM:SS’, where HH is 00..12.


A duration is similar to a timestamp, except that it tells about time elapsed. As such, hours can be larger than 23 and hours may also be specified by a single digit (this, for example, is commonly done in Cisco software).

Examples for durations are “12:05:01”, “0:00:01” and “37:59:59” but not “00:60:00” (HH and MM must still be within the usual range for minutes and seconds).


Valid date/time in RFC3164 format, i.e.: ‘Oct 29 09:47:08’. This parser implements several quirks to match malformed timestamps from some devices.


Valid date/time in RFC5424 format, i.e.: ‘1985-04-12T19:20:50.52-04:00’. Slightly different formats are allowed.


IPv4 address, in dot-decimal notation (AAA.BBB.CCC.DDD).


IPv6 address, in textual notation as specified in RFC4291. All formats specified in section 2.2 are supported, including embedded IPv4 address (e.g. ”::”). Note that a pure IPv4 address (“”) is not valid and as such not recognized.

To avoid false positives, there must be either a whitespace character after the IPv6 address or the end of string must be reached.


The standard (IEEE 802) format for printing MAC-48 addresses in human-friendly form is six groups of two hexadecimal digits, separated by hyphens (-) or colons (:), in transmission order (e.g. 01-23-45-67-89-ab or 01:23:45:67:89:ab ). This form is also commonly used for EUI-64. from:


This parses ArcSight Comment Event Format (CEF) as described in the “Implementing ArcSight CEF” manual revision 20 (2013-06-15).

It matches a format that closely follows the spec. The header fields are extracted into the field name container, all extension are extracted into a container called “Extensions” beneath it.


Rule (compact format):



CEF:0|Vendor|Product|Version|Signature ID|some name|Severity| aa=field1 bb=this is a value cc=field 3


  "f": {
    "DeviceVendor": "Vendor",
    "DeviceProduct": "Product",
    "DeviceVersion": "Version",
    "SignatureID": "Signature ID",
    "Name": "some name",
    "Severity": "Severity",
    "Extensions": {
      "aa": "field1",
      "bb": "this is a value",
      "cc": "field 3"


This supports the LEA on-disk format. Unfortunately, the format is underdocumented, the Checkpoint docs we could get hold of just describe the API and provide a field dictionary. In a nutshell, what we do is extract field names up to the colon and values up to the semicolon. No escaping rules are known to us, so we assume none exists (and as such no semicolon can be part of a value).

If someone has a definitive reference or a sample set to contribute to the project, please let us know and we will check if we need to add additional transformations.


A Cisco interface specifier, as for example seen in PIX or ASA. The format contains a number of optional parts and is described as follows (in ABNF-like manner where square brackets indicate optional parts):

[interface:]ip/port [SP (ip2/port2)] [[SP](username)]

Samples for such a spec are:

  • outside:
  • inside: (
  • outside: (
  • outside:
  • inside: ( (some.user)

Note that the current verision of liblognorm does not permit sole IP addresses to be detected as a Cisco interface spec. However, we are reviewing more Cisco message and need to decide if this is to be supported. The problem here is that this would create a much broader parser which would potentially match many things that are not Cisco interface specs.

As this object extracts multiple subelements, it create a JSON structure.

Let’s for example look at this definiton (compact format):


and assume the following message is to be parsed:

outside: ( (LOCAL\some.user)

Then the resulting JSON will be as follows:

{ "ifaddr": { "interface": "outside", "ip": "", "port": "50179", "ip2": "", "port2": "50179", "user": "LOCAL\\some.user" } }

Subcomponents that are not given in the to-be-normalized string are also not present in the resulting JSON.


Name=value pairs, separated by spaces, as in Netfilter log messages. Name of the selector is not used; names from the line are used instead. This selector always matches everything till end of the line. Cannot match zero characters.


This is an experimental parser. It is used to detect Cisco Interface Specifications. A sample of them is:


Note that this parser does not yet extract the individual parts due to the restrictions in current liblognorm. This is planned for after a general algorithm overhaul.

In order to match, this syntax must start on a non-whitespace char other than colon.


This parses native JSON from the message. All data up to the first non-JSON is parsed into the field. There may be any other field after the JSON, including another JSON section.

Note that any white space after the actual JSON is considered to be part of the JSON. So you cannot filter on whitespace after the JSON.


Rule (compact format):

rule=:%field1:json%interim text %field2:json%'


{"f1": "1"} interim text {"f2": 2}


{ "field2": { "f2": 2 }, "field1": { "f1": "1" } }

Note also that the space before “interim” must not be given in the rule, as it is consumed by the JSON parser. However, the space after “text” is required.


This type permits to specify alternative ways of parsing within a single definition. This can make writing rule bases easier. It also permits the v2 engine to create a more efficient parsing data structure resulting in better performance (to be noticed only in extreme cases, though).

An example explains this parser best:

rule=:a %
         "parser": [
                    {"name":"num", "type":"number"},
                    {"name":"hex", "type":"hexnumber"}
        }% b

This rule matches messages like these:

a 1234 b
a 0xff b

Note that the “parser” parameter here needs to be provided with an array of alternatives. In this case, the JSON array is not interpreted as a sequence. Note, though that you can nest defintions by using custom types.


This parser is used to extract a repeated sequence with the same pattern.

An example explains this parser best:

rule=:a %
        {"name":"numbers", "type":"repeat",
                       {"type":"number", "name":"n1"},
                       {"type":"literal", "text":":"},
                       {"type":"number", "name":"n2"}
                       {"type":"literal", "text":", "}
         }% b

This matches lines like this:

a 1:2, 3:4, 5:6, 7:8 b

and will generate this JSON:

{ "numbers": [
               { "n2": "2", "n1": "1" },
               { "n2": "4", "n1": "3" },
               { "n2": "6", "n1": "5" },
               { "n2": "8", "n1": "7" }

As can be seen, there are two parameters to “alternative”. The parser parameter specifies which type should be repeatedly parsed out of the input data. We could use a single parser for that, but in the example above we parse a sequence. Note the nested array in the “parser” parameter.

If we just wanted to match a single list of numbers like:

a 1, 2, 3, 4 b

we could use this definition:

rule=:a %
        {"name":"numbers", "type":"repeat",
                     {"type":"number", "name":"n"},
                     {"type":"literal", "text":", "}
         }% b

Note that in this example we also removed the redundant single-element array in “while”.

The “while” parameter tells “repeat” how long to do repeat processing. It is specified by any parser, including a nested sequence of parser (array). As long as the “while” part matches, the repetition is continued. If it no longer matches, “repeat” processing is successfully completed. Note that the “parser” parameter must match at least once, otherwise “repeat” fails.

In the above sample, “while” mismatches after “4”, because no ”, ” follows. Then, the parser termiantes, and according to definition the literal ” b” is matched, which will result in a successful rule match (note: the “a ”, ” b” literals are just here for explanatory purposes and could be any other rule element).

Sometimes we need to deal with malformed messages. For example, we could have a sequence like this:

a 1:2, 3:4,5:6, 7:8 b

Note the missing space after “4,”. To handle such cases, we can nest the “alternative” parser inside “while”:

rule=:a %
        {"name":"numbers", "type":"repeat",
                       {"type":"number", "name":"n1"},
                       {"type":"literal", "text":":"},
                       {"type":"number", "name":"n2"}
            "while": {
                        "type":"alternative", "parser": [
                                {"type":"literal", "text":", "},
                                {"type":"literal", "text":","}
         }% b

This definition handles numbers being delemited by either ”, ” or ”,”.

For people with programming skills, the “repeat” parser is described by this pseudocode:

    parse via parsers given in "parser"
    if parsing fails
        abort "repeat" unsuccessful
    parse via parsers given in "while"
while the "while" parsers parsed successfully
if not aborted, flag "repeat" as successful



If set to “True”, permits repeat to accept as successful even when the parser processing failed. This by default is false, and can be set to true to cover some border cases, where the while part cannot definitely detect the end of processing. An example of such a border case is a listing of flags, being terminated by a double space where each flag is delimited by single spaces. For example, Cisco products generate such messages (note the flags part):

Aug 18 13:18:45 %ASA-6-106015: Deny TCP (no connection) from to flags RST  on interface outside


This parses cee syslog from the message. This format has been defined by Mitre CEE as well as Project Lumberjack.

This format essentially is JSON with additional restrictions:

  • The message must start with “@cee:”
  • an JSON object must immediately follow (whitespace before it permitted, but a JSON array is not permitted)
  • after the JSON, there must be no other non-whitespace characters.

In other words: the message must consist of a single JSON object only, prefixed by the “@cee:” cookie.

Note that the cee cookie is case sensitive, so “@CEE:” is NOT valid.


Several rules can have a common prefix. You can set it once with this syntax:

prefix=<prefix match description>

Prefix match description syntax is the same as rule match description. Every following rule will be treated as an addition to this prefix.

Prefix can be reset to default (empty value) by the line:


You can define a prefix for devices that produce the same header in each message. We assume, that you have your rules sorted by device. In such a case you can take the header of the rules and use it with the prefix variable. Here is a example of a rule for IPTables (legacy format, to be converted later):

prefix=%date:date-rfc3164% %host:word% %tag:char-to:-\x3a%:
rule=:INBOUND%INBOUND:char-to:-\x3a%: IN=%IN:word% PHYSIN=%PHYSIN:word% OUT=%OUT:word% PHYSOUT=%PHYSOUT:word% SRC=%source:ipv4% DST=%destination:ipv4% LEN=%LEN:number% TOS=%TOS:char-to: % PREC=%PREC:word% TTL=%TTL:number% ID=%ID:number% DF PROTO=%PROTO:word% SPT=%SPT:number% DPT=%DPT:number% WINDOW=%WINDOW:number% RES=0x00 ACK SYN URGP=%URGP:number%

Usually, every rule would hold what is defined in the prefix at its beginning. But since we can define the prefix, we can save that work in every line and just make the rules for the log lines. This saves us a lot of work and even saves space.

In a rulebase you can use multiple prefixes obviously. The prefix will be used for the following rules. If then another prefix is set, the first one will be erased, and new one will be used for the following rules.

Rule tags

Rule tagging capability permits very easy classification of syslog messages and log records in general. So you can not only extract data from your various log source, you can also classify events, for example, as being a “login”, a “logout” or a firewall “denied access”. This makes it very easy to look at specific subsets of messages and process them in ways specific to the information being conveyed.

To see how it works, let’s first define what a tag is:

A tag is a simple alphanumeric string that identifies a specific type of object, action, status, etc. For example, we can have object tags for firewalls and servers. For simplicity, let’s call them “firewall” and “server”. Then, we can have action tags like “login”, “logout” and “connectionOpen”. Status tags could include “success” or “fail”, among others. Tags form a flat space, there is no inherent relationship between them (but this may be added later on top of the current implementation). Think of tags like the tag cloud in a blogging system. Tags can be defined for any reason and need. A single event can be associated with as many tags as required.

Assigning tags to messages is simple. A rule contains both the sample of the message (including the extracted fields) as well as the tags. Have a look at this sample:

rule=:sshd[%pid:number%]: Invalid user %user:word% from %src-ip:ipv4%

Here, we have a rule that shows an invalid ssh login request. The various fields are used to extract information into a well-defined structure. Have you ever wondered why every rule starts with a colon? Now, here is the answer: the colon separates the tag part from the actual sample part. Now, you can create a rule like this:

rule=ssh,user,login,fail:sshd[%pid:number%]: Invalid user %user:word% from %src-ip:ipv4%

Note the “ssh,user,login,fail” part in front of the colon. These are the four tags the user has decided to assign to this event. What now happens is that the normalizer does not only extract the information from the message if it finds a match, but it also adds the tags as metadata. Once normalization is done, one can not only query the individual fields, but also query if a specific tag is associated with this event. For example, to find all ssh-related events (provided the rules are built that way), you can normalize a large log and select only that subset of the normalized log that contains the tag “ssh”.

Log annotations

In short, annotations allow to add arbitrary attributes to a parsed message, depending on rule tags. Values of these attributes are fixed, they cannot be derived from variable fields. Syntax is as following:

annotate=<tag>:+<field name>="<field value>"

Field value should always be enclosed in double quote marks.

There can be multiple annotations for the same tag.


Look at sample rulebase for configuration examples and matching log lines. Note that the examples are currently in legacy format, only.

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