Suricata and Prometheus Integration

Input and output integration overview

This plugin reports internal performance counters of the Suricata IDS/IPS engine and processes the incoming data to fit Telegraf’s format.

The Prometheus Output Plugin enables Telegraf to expose metrics at an HTTP endpoint for scraping by a Prometheus server. This integration allows users to collect and aggregate metrics from various sources in a format that Prometheus can process efficiently.

Integration details

Suricata

The Suricata plugin captures and reports internal performance metrics from the Suricata IDS/IPS engine, which includes a wide range of statistics such as traffic volume, memory usage, uptime, and counters for flows and alerts. This plugin listens for JSON-formatted log outputs from Suricata, allowing it to parse and format the data for integration with Telegraf. It operates as a service input plugin, meaning it actively waits for metrics or events from Suricata rather than collecting metrics at predefined intervals. The plugin supports configurations for different metrics versions allowing for enhanced flexibility and detailed data gathering.

Prometheus

This plugin for facilitates the integration with Prometheus, a well-known open-source monitoring and alerting toolkit designed for reliability and efficiency in large-scale environments. By working as a Prometheus client, it allows users to expose a defined set of metrics via an HTTP server that Prometheus can scrape at specified intervals. This plugin plays a crucial role in monitoring diverse systems by allowing them to publish performance metrics in a standardized format, enabling extensive visibility into system health and behavior. Key features include support for configuring various endpoints, enabling TLS for secure communication, and options for HTTP basic authentication. The plugin also integrates seamlessly with global Telegraf configuration settings, supporting extensive customization to fit specific monitoring needs. This promotes interoperability in environments where different systems must communicate performance data effectively. Leveraging Prometheus’s metric format, it allows for flexible metric management through advanced configurations such as metric expiration and collectors control, offering a sophisticated solution for monitoring and alerting workflows.

Configuration

Suricata

[[inputs.suricata]]
  ## Source
  ## Data sink for Suricata stats log. This is expected to be a filename of a
  ## unix socket to be created for listening.
  # source = "/var/run/suricata-stats.sock"

  ## Delimiter
  ## Used for flattening field keys, e.g. subitem "alert" of "detect" becomes
  ## "detect_alert" when delimiter is "_".
  # delimiter = "_"

  ## Metric version
  ## Version 1 only collects stats and optionally will look for alerts if
  ## the configuration setting alerts is set to true.
  ## Version 2 parses any event type message by default and produced metrics
  ## under a single metric name using a tag to differentiate between event
  ## types. The timestamp for the message is applied to the generated metric.
  ## Additional tags and fields are included as well.
  # version = "1"

  ## Alerts
  ## In metric version 1, only status is captured by default, alerts must be
  ## turned on with this configuration option. This option does not apply for
  ## metric version 2.
  # alerts = false

Prometheus

[[outputs.prometheus_client]]
  ## Address to listen on.
  ##   ex:
  ##     listen = ":9273"
  ##     listen = "vsock://:9273"
  listen = ":9273"

  ## Maximum duration before timing out read of the request
  # read_timeout = "10s"
  ## Maximum duration before timing out write of the response
  # write_timeout = "10s"

  ## Metric version controls the mapping from Prometheus metrics into Telegraf metrics.
  ## See "Metric Format Configuration" in plugins/inputs/prometheus/README.md for details.
  ## Valid options: 1, 2
  # metric_version = 1

  ## Use HTTP Basic Authentication.
  # basic_username = "Foo"
  # basic_password = "Bar"

  ## If set, the IP Ranges which are allowed to access metrics.
  ##   ex: ip_range = ["192.168.0.0/24", "192.168.1.0/30"]
  # ip_range = []

  ## Path to publish the metrics on.
  # path = "/metrics"

  ## Expiration interval for each metric. 0 == no expiration
  # expiration_interval = "60s"

  ## Collectors to enable, valid entries are "gocollector" and "process".
  ## If unset, both are enabled.
  # collectors_exclude = ["gocollector", "process"]

  ## Send string metrics as Prometheus labels.
  ## Unless set to false all string metrics will be sent as labels.
  # string_as_label = true

  ## If set, enable TLS with the given certificate.
  # tls_cert = "/etc/ssl/telegraf.crt"
  # tls_key = "/etc/ssl/telegraf.key"

  ## Set one or more allowed client CA certificate file names to
  ## enable mutually authenticated TLS connections
  # tls_allowed_cacerts = ["/etc/telegraf/clientca.pem"]

  ## Export metric collection time.
  # export_timestamp = false

  ## Specify the metric type explicitly.
  ## This overrides the metric-type of the Telegraf metric. Globbing is allowed.
  # [outputs.prometheus_client.metric_types]
  #   counter = []
  #   gauge = []

Input and output integration examples

Suricata

1. Network Traffic Analysis: Utilize the Suricata plugin to track detailed metrics about network intrusion attempts and performance, aiding in real-time threat detection and response. By visualizing captured alerts and flow statistics, security teams can quickly pinpoint vulnerabilities and mitigate risks.

2. Performance Monitoring Dashboard: Create a dashboard using the Suricata Telegraf plugin metrics to monitor the health and performance of the IDS/IPS engine. This use case provides an overview of memory usage, captured packets, and alert statistics, allowing teams to maintain optimal operating conditions.

3. Automated Security Reporting: Leverage the plugin to generate regular reports on alert statistics and traffic patterns, helping security analysts to identify long-term trends and prepare strategic defense initiatives. Automated reports also ensure that the security posture of the network is continually assessed.

4. Real-time Alert Handling: Integrate Suricata’s alert metrics within a broader incident response automation framework. By incorporating the inputs from the Suricata plugin, organizations can develop smart triggers for alerting and automated response workflows that enhance reaction times to potential threats.

Prometheus

1. Monitoring Multi-cloud Deployments: Utilize the Prometheus plugin to collect metrics from applications running across multiple cloud providers. This scenario allows teams to centralize monitoring through a single Prometheus instance that scrapes metrics from different environments, providing a unified view of performance metrics across hybrid infrastructures. It streamlines reporting and alerting, enhancing operational efficiency without needing complex integrations.

2. Enhancing Microservices Visibility: Implement the plugin to expose metrics from various microservices within a Kubernetes cluster. Using Prometheus, teams can visualize service metrics in real time, identify bottlenecks, and maintain system health checks. This setup supports adaptive scaling and resource utilization optimization based on insights generated from the collected metrics. It enhances the ability to troubleshoot service interactions, significantly improving the resilience of the microservice architecture.

3. Real-time Anomaly Detection in E-commerce: By leveraging this plugin alongside Prometheus, an e-commerce platform can monitor key performance indicators such as response times and error rates. Integrating anomaly detection algorithms with scraped metrics allows the identification of unexpected patterns indicating potential issues, such as sudden traffic spikes or backend service failure. This proactive monitoring empowers business continuity and operational efficiency, minimizing potential downtimes while ensuring service reliability.

4. Performance Metrics Reporting for APIs: Utilize the Prometheus Output Plugin to gather and report API performance metrics, which can then be visualized in Grafana dashboards. This use case enables detailed analysis of API response times, throughput, and error rates, promoting continuous improvement of API services. By closely monitoring these metrics, teams can quickly react to degradation, ensuring optimal API performance and maintaining a high level of service availability.

Feedback

Thank you for being part of our community! If you have any general feedback or found any bugs on these pages, we welcome and encourage your input. Please submit your feedback in the InfluxDB community Slack.