Kafka SIEM Architecture

https://digitalis.io
info@digitalis.io
Security Information and
Event Management with Kafka,
Kafka Connect, KSQL and Logstash

2
Jason Bell
ABOUT
Working with Kafka since 2014, in
development, support and now DevOps.
Author of Machine Learning: Hands on
for Developers and Technical
Professionals, published by Wiley.
Kafka DevOps Engineer

©2020 digitalis.io Ltd. Do not distribute without
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What is SIEM?

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SIEM adoption originally driven from Payment Card
Industry Data Security Standard (PCI DSS).

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Data can come from various sources such as
firewalls, anti-virus, login information and intrusion
prevention systems.

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For example: A user does 20 failed login attempts.
Has the user actually forgotten? Let’s class this as
a low priority event. The user may have just
forgotten their password and retried.

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A user does 140 failed login attempts in five
minutes. This is more than likely a brute force
attack and needs investigating.

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Enterprise SIEM Problems

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● Large Volumes of Data.

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● Variety of log formats - RFC5424, RFC3164, Windows Events and other
bespoke log formats from network devices.

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● Regulatory compliance.

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“Virtually every regulatory compliance regime or
standard such as GDPR, ISO 27001, PCI DSS,
HIPAA, FERPA, Sarbanes-Oxley (SOX), FISMA,
and SOC 2 have some requirements of log
management to preserve audit trails of activity that
addresses the CIA (Confidentiality, Integrity, and
Availability) triad.”
https://digitalis.io/blog/kafka/apache-kafka-and-regulatory-compliance/

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● High Availability Requirements

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● Downstream sometimes cannot keep up at peak times – 9am, DDoS events

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● Downstream sometimes cannot keep up at peak times – 9am, DDoS events
● Multiple consumers of data and connectivity to them
○ routing, transforming, filtering

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Why use Kafka?

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Why Kafka?
● High Availability

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Why Kafka?
● Scalable

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Why Kafka?
● Scalable
● High Throughput

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Why Kafka?
● Scalable
● High Throughput
● Rich Ecosystem

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Why Kafka?
● Scalable
● High Throughput
● Rich Ecosystem
● ksqlDB for Implementing Logic for Routing/Filtering/Transforming

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Why Kafka?
● Scalable
● High Throughput
● Rich Ecosystem
● ksqlDB for Implementing Logic for Routing/Filtering/Transforming
● Buffering of data during high peak volumes – a shock absorber.

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Kafka SIEM Architecture

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Data Flows and Components

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Topic and Outbound Data Flows

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Data Ingestion

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Data Ingestion
● Non-repudiation - fingerprinting source logs
● Transformation to JSON
● Non-standard syslog formats - bespoke grokking

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Logstash - Input

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TODO: Insert Logstash In->Filter-Out diagram

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Logstash Input – All Types input {
udp {
host => "0.0.0.0"
port => 5140
type => rfc5424
tags => ["rfc5424"]
}
tcp {
host => "0.0.0.0"
port => 5140
type => rfc5424
tags => ["rfc5424"]
}
syslog {
port => 5150
type => rfc3164
tags => ["rfc3164"]
}
}

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Logstash - Filtering

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Logstash Filter – RFC3164
filter {
if [type] == "rfc3164" {
# rename and remove fields
mutate {
remove_field => [ "@version", "@timestamp" ]
rename => { "host" => "client_addr" }
rename => { "logsource" => "host" }
rename => { "severity_label" => "severity" }
rename => { "facility_label" => "facility" }
}
}
}
}

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Logstash Filter – RFC5424
filter {
if [type] == "rfc5424" {
# parse RFC5424 log
grok {
patterns_dir => "/etc/logstash/patterns"
match => [ "message", "%{SYSLOG}" ]
tag_on_failure => [ "_grokparsefailure_syslog" ]
}
# rename fields and remove unneeded ones
mutate {
rename => { "syslog_facility" => "facility" }
rename => { "syslog_severity" => "severity" }
# message_syslog contains message content +
extra data
replace => { "message" => "%{message_syslog}" }
remove_field => [ "@version", "facility_label",
"@timestamp", "message_content", "message_syslog" ]
rename => { "program" => "ident" }
rename => { "timestamp_source" => "timestamp"}
rename => { "host" => "client_addr" }
rename => { "host_source" => "host" }
}

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Logstash Filter – RFC JSON

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{
"host":“testhost",
"ident":"info",
"message":"01070417:6: AUDIT - user admin - RAW: httpd(pam_audit): User=admin tty=(unknown) host=10.234.254.90 failed to login after 1 attempt….",
"priority":"info",
"facility":"local0",
"client_addr":"10.234.254.90",
"bucket":"2019042913",
"evt_id":"33a3a040-6a7f-11e9-a8be-0050568115fd",
"extradata":"[ ]",
"fingerprint ":"73dd765f55a1791b667bd6160235e3f6 ",
"rawdata ":"..... ",
"pid":"-",
"msgid":"-",
"timestamp":"2019-04-29T14:03:37.000000Z"
}

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Logstash - Output

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output {
if "syslog_rfc5424" in [tags] {
kafka {
codec => json
topic_id => "syslog_rfc5424"
bootstrap_servers => "{{ confluent_ksql_bootstrap_servers }}"
security_protocol => SSL
ssl_key_password => "{{ logstash_ssl_key_password }}"
ssl_keystore_location => "/etc/logstash/logstash.keystore.jks"
ssl_keystore_password => "{{ logstash_ssl_keystore_password }}"
ssl_truststore_location => "/etc/logstash/logstash.truststore.jks"
ssl_truststore_password => "{{ logstash_ssl_truststore_password }}"
compression_type => "snappy"
acks => "1"
retries => "3"
retry_backoff_ms => "500"
request_timeout_ms => "2000"
batch_size => "32768"
ssl_endpoint_identification_algorithm => "https"
ssl_keystore_type => jks
}
}
}

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Topic Filtering and Routing

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Filter / Routing
● Some downstream systems are not interested in INFO -
too much data

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Filter / Routing
● Some downstream systems are not interested in INFO -
too much data
● Some are only interested in Windows events for
example.

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create stream syslog_rfc3164 (client_addr varchar, host varchar, timestamp varchar, severity varchar,
message varchar, facility varchar, type varchar, priority varchar) with (KAFKA_TOPIC='syslog_rfc3164',
VALUE_FORMAT='JSON’);
create stream auth_rfc3164 with (KAFKA_TOPIC='syslog_auth', VALUE_FORMAT='JSON') AS SELECT * FROM
syslog_rfc3164 WHERE message LIKE '%password check failed for user%' OR message LIKE '%An account
failed to log on.%' OR message LIKE '%%0xC000006D’;
create stream syslog_rfc5424 (facility varchar, message varchar, pid varchar, type varchar, timestamp
varchar, ident varchar, client_addr varchar, host varchar, msgid varchar, extradata varchar, priority
varchar) with (KAFKA_TOPIC='syslog_rfc5424', VALUE_FORMAT='JSON’);
create stream auth_rfc5424 with (KAFKA_TOPIC='syslog_auth', VALUE_FORMAT='JSON') AS SELECT * FROM
syslog_rfc5424 WHERE message LIKE '%password check failed%' OR extradata LIKE '%|309|%' OR message
LIKE '%An account failed to log on.%' OR message LIKE '%%0xC000006D';

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Destinations and Sinks

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Destinations and Sink
● Use existing connectors

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Destinations and Sink
● Use existing connectors
● Build your own connectors

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Splunk HTTP Sink in
Kafka Connect

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{
"name": "syslog-sink-splunk",
"config": {
"connector.class": "SplunkHECSinkConnector",
"tasks.max": "{{ tasks_max }}",
"topics": "{{ topics }}",
"splunk.endpoint.uri": "{{ splunk_endpoint_uri }}",
"splunk.hec.token": "{{ splunk_hec_token }}",
"splunk.index": "{{ splunk_index }}",
"splunk.channelid": "{{ splunk_channelid }}",
"splunk.sourcetype": "{{ splunk_sourcetype }}",
"splunk.http.loglevel": "{{ splunk_http_loglevel }}",
"value.converter": "org.apache.kafka.connect.json.JsonConverter",
"value.converter.schemas.enable": "{{ splunk_value_converter_schemas_enable }}",
"errors.tolerance": "{{ splunk_errors_tolerance }}",
"errors.deadletterqueue.topic.name":"{{ errors_deadletterqueue_topic_name }}",
"errors.deadletterqueue.topic.replication.factor": "{{ errors_deadletterqueue_topic_replication_factor }}"
}
}

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Testing

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Testing
● Process 3TB/day data volumes.

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Testing
● Process 3TB/day data volumes.
● Prove the solution can scale horizontally.

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Testing: Process 2.3TB/day data volumes.
● 3TB/day = 33MB/second

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• 400 threads were set up in the Thread Group to simulate
400 servers sending the logs.

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• 6 load injectors were setup, totalling 2400 threads
(simulated servers), in order to generate between
20MB/second to 40 MB/second load against the endpoint
from the injectors.

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• The load was injected over 5 days period at a sustained
rate to ascertain the performance characteristics of each
component over a prolonged duration.

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Carry on the conversation:
• Website: https://digitalis.io
• Reddit: https://reddit.com/users/digitalis_io
• Twitter: @digitalis_io

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Any Questions?

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