Regex Log Parser


For text log types with more complex structure, you can use the regex parser.

The regex parser uses named groups in regular expressions to extract field values from each line of text. You can use grok syntax (i.e. %{PATTERN_NAME:field_name}) to build complex expressions taking advantage of the built-in patterns provided by Panther or by defining your own.

Panther's log processor uses the RE2 syntax for regular expressions. RE2 does not support some operations common to other regular expression engines, such as lookbehind. Be sure to check any expressions or grok patterns you copy/paste from other systems.

For example to match the text

2020-10-10T14:32:05 [[email protected]] [DEBUG] "" Something when wrong

We can use this grok syntax with this pattern:

%{NOTSPACE:timestamp} \[%{WORD:service}@%{DATA:ip}\] \[%{WORD:log_level}\] %{GREEDYDATA:message}

Which is the rough equivalent of this 'raw' regular expression:

(?P<timestamp>\S+) \[(?P<service>\w+)@(?P<ip>.*?)\] \[(?P<log_level>\w+)\] (?P<message>.*)

For best performance stick to simple built-in patterns such as DATA, NOTSPACE, GREEDYDATA and WORD. Avoid complex expressions unless it is required to distinguish the field name based on the value (e.g. (%{IP:ip_address}|%{WORD:username})


Using the regex parser we will define a log type for Juniper.Audit logs. Panther already supports these logs natively, but we will be using them here because they have variable conflicting forms and can only be 'solved' by using regex parser.

The sample logs for Juniper.Audit are:

Jan 22 16:14:23 my-jwas [mws-audit][INFO] [mykonos] [] Logged in successfully
Jan 23 19:16:22 my-jwas [mws-audit][INFO] [ea77722a8516b0d1135abb19b1982852] Deactivate response 1832840420318015488
Feb 7 20:29:51 my-jwas [mws-audit][INFO] [mykonos] [] Login failed. Attempt: 1
Feb 14 19:02:54 my-jwas [mws-audit][INFO][mykonos] Changed configuration parameters: services.spotlight.enabled, services.spotlight.server_address
      JUNIPER_TIMESTAMP: '[A-Z][a-z]{2} \d?\d \d\d:\d\d:\d\d'
      # An apikey is composed of 32 hex characters
      API_KEY: '[a-fA-F0-9]{32}'
    # We will be splitting the pattern in multiple parts so we can add comments helping us debug it in the future.
    # All parts are concatenated into a single pattern by Panther WITHOUT ADDING SPACES BETWEEN PARTS.
    # If you don't want to split your patterns just use an array with a single string.
    # The log line starts with a timestamp (captured as 'timestamp')
    - '^%{JUNIPER_TIMESTAMP:timestamp}'
    # Followed by this static text
    - ' my-jwas \[mws-audit\]'
    # Then comes the log level surrounded by square brackets and optional space (captured as 'log_level')
    - '\[%{DATA:log_level}\] ?' 
    # After it, we get either an api key or a user name, surrounded by square brackets,
    # which we capture as 'apikey' or 'username' depending on the match
    - '\[(%{API_KEY:apikey}|%{USERNAME:username})\] '
    # Optionally followed by the ip address of the request in square brackets (captured as 'request_ip')
    # Note that we use 'DATA' instead of the specific 'IP' named pattern. 
    # It is not needed because 'request_ip' is always at this position and we are certain of the log type match
    # due to the distinctive ' my-jwas [mws-audit]' literal.
    - '(\[%{DATA:request_ip}\])?'
    # And finally the rest of the line is the message (captured as 'message')
    - '%{GREEDYDATA:message}'
    trimSpace: true # We want to trim the space of the message
- name: timestamp
  type: timestamp
  required: true
   - '%b %d %H:%M:%S'
  isEventTime: false # the timestamps have no year so we cannot use them as partition time
- name: log_level
  type: string
  required: true
- name: apikey
  type: string
- name: username
  type: string
- name: request_ip
  type: string
  indicators: [ip]
- name: message
  type: string

Built-in regex pattern reference

The following tables detail the built-in Panther regex patterns you can use.







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