Skip to main content
Network Traffic Analysis in ELK Stack

Network Traffic Analysis in ELK Stack

$249.00
Toolkit Included:
Includes a practical, ready-to-use toolkit containing implementation templates, worksheets, checklists, and decision-support materials used to accelerate real-world application and reduce setup time.
Your guarantee:
30-day money-back guarantee — no questions asked
How you learn:
Self-paced • Lifetime updates
When you get access:
Course access is prepared after purchase and delivered via email
Who trusts this:
Trusted by professionals in 160+ countries
Adding to cart… The item has been added

This curriculum spans the technical rigor of a multi-workshop program for building and hardening a production-grade network traffic analysis system in the ELK Stack, comparable to an internal capability build led by a security engineering team integrating packet data at scale.

Module 1: Architecting Scalable Data Ingestion Pipelines

  • Configure Logstash to parse NetFlow v5/v9 and IPFIX using the netflow codec, ensuring template cache timeouts align with exporter refresh rates.
  • Deploy Filebeat on network probes to forward pcap-derived JSON logs, adjusting harvest_buffer_size to prevent memory exhaustion during traffic bursts.
  • Implement Kafka as a buffering layer between packet analyzers and Logstash to absorb ingestion spikes during DDoS events.
  • Select between ECS-compliant custom parsing versus leveraging Elastic’s built-in NetFlow pipelines based on field normalization requirements.
  • Size Logstash worker threads and output queue capacities to sustain 10 Gbps packet capture replay loads without backpressure.
  • Enforce TLS 1.3 and mutual authentication between Beats agents and Logstash forwarders in regulated environments.

Module 2: Packet Capture Integration and Metadata Enrichment

  • Integrate Zeek (Bro) logs with Suricata alerts using a shared time-based index naming strategy for cross-correlation in Kibana.
  • Map VLAN IDs and interface descriptions from NetFlow data to switchport configurations using a static CSV lookup in Logstash.
  • Enrich flow records with geolocation data from MaxMind DBs, managing license updates and fallback mechanisms for stale entries.
  • Attach application-layer protocol identifiers from Zeek’s http.log to NetFlow sessions using 5-tuple and timestamp joins.
  • Apply conditional parsing in ingest pipelines to extract DNS query/response fields only from relevant UDP flows.
  • Suppress redundant internal multicast flows in parsing rules to reduce index volume without losing anomaly detection capability.

Module 3: Index Design and Lifecycle Management

  • Define time-based index templates with daily rollover for flow data, applying ILM policies that transition to warm tier after 7 days.
  • Separate high-cardinality Zeek logs (e.g., http, dns) into dedicated index patterns to prevent mapping explosions.
  • Set shard allocation based on node roles—assign hot data to SSD-backed data_hot nodes and archive to data_content tiers.
  • Calculate shard count per index using ingest rate and retention period, capping at 50GB per shard to maintain search performance.
  • Implement field aliasing for renamed NetFlow fields to maintain dashboard compatibility during schema evolution.
  • Disable _source for raw packet metadata indices and enable stored_fields for critical search terms to reduce storage costs.

Module 4: Real-Time Detection and Alerting Logic

  • Construct Elastic Rules to detect beaconing behavior using frequency thresholds on destination IP connections over 24-hour sliding windows.
  • Correlate Suricata TLS handshake alerts with Zeek x509.log to identify self-signed certificates in outbound traffic.
  • Configure rule execution intervals to balance detection latency and cluster load during peak ingestion.
  • Use exception lists to suppress known false positives, such as backup traffic to cloud storage endpoints.
  • Trigger alerts on asymmetric routing patterns by comparing ingress and egress interface pairs in bidirectional NetFlow.
  • Validate detection logic using historical PCAP replays in a staging environment before production deployment.

Module 5: Performance Optimization and Cluster Resilience

  • Tune Elasticsearch refresh_interval for traffic indices from 1s to 30s to reduce segment load during bulk imports.
  • Isolate heavy aggregation queries from real-time dashboards using dedicated query coordinator nodes.
  • Implement circuit breakers for field data and request memory to prevent OOM crashes during misbehaving searches.
  • Configure index-level throttling on snapshot operations to avoid degrading search performance during backups.
  • Deploy dedicated master-eligible nodes with minimum_master_nodes set to prevent split-brain during network partitions.
  • Use shard request cache selectively on frequently accessed dashboards with static time ranges.

Module 6: Secure Access and Audit Compliance

  • Map LDAP groups to Kibana roles, restricting packet payload visibility to Tier-3 SOC analysts via document-level security.
  • Enable audit logging in Elasticsearch to track search queries and configuration changes for PCI DSS compliance.
  • Mask sensitive fields (e.g., HTTP user agents, URIs) in dashboards using Kibana field formatters for non-security teams.
  • Rotate API keys for automation scripts quarterly using Elastic’s API key management endpoints.
  • Enforce FIPS 140-2 compliant cipher suites across Kibana, Elasticsearch, and Logstash in government deployments.
  • Integrate with SIEM workflows by forwarding Elastic alerts to external SOAR platforms via webhook with HMAC signing.

Module 7: Advanced Traffic Behavior Analytics

  • Build machine learning jobs to model baseline bandwidth consumption per subnet and flag deviations exceeding 3σ.
  • Use Kibana’s time series explorer to isolate IoT device traffic spikes coinciding with external NTP server queries.
  • Apply community detection algorithms in graph explorers to uncover hidden peer-to-peer mesh networks.
  • Compare DNS query volume to resolution success rates to detect DGA-based malware command channels.
  • Cluster SSH login sources by geographic anomaly score using ML-driven geolocation deviation detection.
  • Validate behavioral baselines using labeled red team exercise data to calibrate false positive rates.

Module 8: Cross-Tool Validation and Operational Drills

  • Validate Elastic-derived flow metrics against router NetFlow exports using SNMP-based byte counters.
  • Reproduce detection alerts in Wireshark using saved filters derived from Elastic query DSL.
  • Conduct quarterly failover tests by isolating master nodes and verifying cluster rebalancing without data loss.
  • Simulate index corruption by manually altering segment files and validating snapshot restore procedures.
  • Compare ML job anomaly scores with Zeek intel.log hits to assess detection coverage gaps.
  • Run packet capture replay attacks in isolated lab environments to test end-to-end detection and alerting latency.
!