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Data source | Description | Collector service name | Devo table | Available from release |
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Any | Theoretically any source you send to an SQS can be collected |
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CONFIG LOGS |
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AWS ELB |
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AWS ALB |
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CISCO UMBRELLA |
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CLOUDFLARE LOGPUSH |
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CLOUDFLARE AUDIT |
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CLOUDTRAIL |
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CLOUDTRAIL VIA KINESIS FIREHOSE |
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CLOUDWATCH |
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CLOUDWATCH VPC |
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CONTROL TOWER | VPC Flow Logs, Cloudtrail, Cloudfront, and/or AWS config logs |
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FDR |
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GUARD DUTY |
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GUARD DUTY VIA KINESIS FIREHOUSE |
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IMPERVA INCAPSULA |
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LACEWORK |
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PALO ALTO |
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ROUTE 53 |
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OS LOGS |
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SENTINEL ONE FUNNEL |
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S3 ACCESS |
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VPC LOGS |
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WAF LOGS |
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Options
See examples of common configurations here: General S3 Collector Configuration Examples and Recipes
There are many configurable options outlined in the README on the GitLab link, reproduced here. See GitLab repository for specific examples in each subdirectory.
direct_mode --- true or false (default is false), set to true if the logs are being sent directly to the queue without using s3.
file_field_definitions --- defined as a dictionary mapping variable names (you decide) to lists of parsing rules.
each parsing rule has an operator, with its own keys which go along with it. Parsing rules are applied in the order they are listed in the configuration.The "split" operator takes an "on" and an "element" -- the file name will split into pieces on the character or character sequence specified by "on" and extract whatever is at the specified "element" index as in the example.
the "replace" operator take a "to_replace" and a "replace_with"
For example, if your filename were "
server_logs/12409834/ff.gz
", this configuration would store the log_type as "serverlogs
"
Code Block |
---|
"file_field_definitions":
{
"log_type": [{"operator": "split", "on": "/", "element": 0}, {"operator": "replace", "to_replace": "_", "replace_with": ""}]
} |
filename_filter_rules: a list of rules for filtering out entire files.
encoding -- takes a string from one of the following: “gzip” “none” “parquet”
ack_messages -- whether or not to delete messages from the queue after processing, takes boolean values. If not specified, default is true. We recommend leaving this out of the config. If you see it in there, pay close attention to if it’s on or off.
file_format -- takes a dictionary with the following keys
type -- a string specifying which processor to use
single_json_object -- logs are stored as/in a json object
single_json_object_processor config options: “key” (string: the key of where the list of logs is stored) See cloudtrail_collector for example.
Code Block config: {"key": "log"} fileobj: {..."log": {...}}
unseparated_json_processor -- logs are stored as/in json objects which are written in a text file with no separator
unseparated_json config options: “key” (string: where the log is stored), “include” (dict: maps names of keys outside of inner part to be included, which can be renamed). If there is no key, that is, the whole JSON object is the desired log, set “flat”: true See aws_config_collector for example
Code Block fileobj: {...}{...}{...}
text_file_processor -- logs are stored as text files, potentially with lines and fields separated with e.g. commas and newlines
text_file config options: includes options for how lines and records are separated (e.g. newline, tab, comma), good for csv style data.
line_split_processor –- logs stored in a newline separated file, works more quickly than separated_json_processor
config options: “json”: true or false. If setting json to true, assumes that logs are newline-separated json, and allows them to be parsed by the collector therefore enabling record-field mapping
separated_json_processor – logs stored as many json objects that have some kind of separator
config options: specify the separator e.g. “separator”: “||”. the default is newline if left unused.
Code Block fileobj: {...}||{...}||{...}
jamf_processor – special processor for JAMF logs
aws_access_logs_processor – special processor for AWS access logs
windows_security_processor – special processor for Windows Security logs
vpc_flow_processor – special processor for VPC Flow logs
json_line_arrays_processor – processor for unseparated json objects that are on multiple lines of a single file
Code Block fileobj: {...}{...} {...}{...}{...} {...}
dict_processor – processor for logs that comes as python dictionary objects, i.e. in direct mode
config -- a dictionary of information the specified file_format processor needs
record_field_mapping -- a dictionary -- each key defines a variable that can be parsed out from each record (which may be referenced later in filtering)
e.g., we may want to parse something and call it "type", by getting "type" from a certain key in the record (which may be multiple layers deep).Code Block {"type": {"keys": ["file", "type"]}, "operations": [] }
keys is a list of how key values in the record to look into to find the value, its to handle nesting (essentially defining a path through the data). Suppose we have logs that look like this:
Code Block {“file”: {“type”: { “log_type” : 100}}}
so if we want to get the log_type, we should list all the keys needed to parse through the json in order:
Code Block keys: [“file”, “type”, “log_type”]
In many cases you will probably only need one key.
e.g. in flat json that isn’t nested
Code Block {“log_type”: 100, “other_info”: “blah” ….}
here you would just specify keys: [“log_type”]. A few operations are supported that can be used to further alter the parsed information (like split and replace). This snippet would grab whatever is located at log[“file”][“type”] and name it as “type”. record_field_mapping defines variables by taking them from logs, and these variables can then be used for filtering. Let’s say you have a log in json format like this which will be set to devo:
Code Block {“file”: {“value”: 0, “type”: “security_log”}}
Specifying “type” in the record_field_mapping will allow the collector to extract that value, “security_log” and save it as type. Now let’s say you want to change the tag dynamically based on that value. You could change the routing_template to something like my.app.datasource.[record-type]. In the case of the log above, it would be sent to my.app.datasource.security_log. Now let’s say you want to filter out (not send) any records which have the type security_log. You could write a line_filter_rule as follows:
{"source": "record", "key": "type", "type": "match", "value": "security_log" }
We specified the source as record because we want to use a variable from the record_field_mapping. We specified the key as “type” because that is the name of the variable we defined. We specify type as “match” because any record matching this rule we want to filter out. And we specify the value as security_log because we specifically do not want to send any records with the type equalling “security_log” The split operation is the same as if you ran the python split function on a string.Let’s say you have a filename “logs/account_id/folder_name/filename” and you want to save the account_id as a variable to use for tag routing or filtering.
You could write a file_field_definition like this:
"account_id": [{"operator": "split", "on": "/", "element": 1}]
This would store a variable called account_id by taking the entire filename and splitting it into pieces based on where it finds backslashes, then take the element as position one. In Python it would look like:
Code Block filename.split(“/”)[1]
routing_template -- a string defining how to build the tag to send each message. e.g.
"my.app.wow.[record-type].[file-log_type]" -- if the "type" extracted during record_field_mapping were "null", the record would be sent to the tag "my.app.wow.null"line_filter_rules -- a list of lists of rules for filtering out individual records so they do not get sent to devo
for example:
Code Block |
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"line_filter_rules": [
[{
"source": "record",
"key": "type",
"type": "doesnotmatch",
"value": "ldap"
}],
[
{"source": "file", "key": "main-log_ornot", "type": "match", "value": "main-log"},
{"source": "record", "key": "type", "type": "match", "value": "kube-apiserver-audit"},
]
] |
This set of rules could be expressed in pseudocode as follows:if record.type != "ldap" OR (file.main-log_ornot == main-log AND record.type == "kube-api-server-audit"):
do_not_send_record()
(Internal) Notes + Debugging
Config can include "debug_mode": true to print out some useful information as logs come in.
For local testing it is useful to set "ack_messages" to false, to try processing without eating from the queue. Be careful to remove this or set it to true when launching the collector. The default is to ack messages if it is not set.
If something seems wrong at launch, you can set the following in the collector parameters/ job config.
"debug_mode": true,
"do_not_send": true,
"ack_messages": false
...
Run the collector
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This data collector can be run in any machine that has the Docker service available because it should be executed as a docker container. The following sections explain how to prepare all the required setup for having the data collector running. StructureThe following directory structure should be created for being used when running the collector:
Devo credentialsIn Devo, go to Administration → Credentials → X.509 Certificates, download the Certificate, Private key and Chain CA and save them in
Editing the config.yaml file
Replace the placeholders with your required values following the description table below:
Download the Docker imageThe collector should be deployed as a Docker container. Download the Docker image of the collector as a .tgz file by clicking the link in the following table:
Use the following command to add the Docker image to the system:
The Docker image can be deployed on the following services: DockerExecute the following command on the root directory
Docker ComposeThe following Docker Compose file can be used to execute the Docker container. It must be created in the
To run the container using docker-compose, execute the following command from the
We use a piece of software called Collector Server to host and manage all our available collectors. To enable the collector for a customer:
Editing the JSON configuration
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