The Elastic Stack As A SIEM Solution
Last updated
Last updated
The Elastic Stack (comprising Beats, Logstash, Elasticsearch, and Kibana) can be effectively implemented as a Security Information and Event Management (SIEM) solution. This setup enables organizations to collect, store, analyze, and visualize security-related data from various sources, such as firewalls, Intrusion Detection/Prevention Systems (IDS/IPS), endpoints, and more. Below is a detailed explanation of how the Elastic Stack functions as a SIEM solution, with a focus on Kibana Query Language (KQL) for querying and analyzing security data.
Data Ingestion:
Security-related logs and events are ingested into the Elastic Stack using tools like Beats or Logstash.
Examples of data sources include:
Firewalls: Network traffic logs.
IDS/IPS: Alerts for suspicious activity.
Endpoints: Logs from antivirus software, EDR tools, or operating systems.
Logstash processes and enriches the data before sending it to Elasticsearch.
Data Storage and Indexing:
Elasticsearch stores and indexes the security data, enabling fast searches and correlations.
Security events are indexed based on attributes like timestamps, event codes, source IPs, and usernames.
Data Analysis and Visualization:
Kibana serves as the primary interface for SOC analysts to query, analyze, and visualize security data.
Custom dashboards and visualizations provide insights into security-related events, helping analysts detect and respond to incidents.
Threat Detection:
Elasticsearch performs searches and correlations on the collected data to identify patterns indicative of threats.
Example: Detecting brute-force attacks by correlating multiple failed login attempts from the same IP address.
Incident Response:
SOC analysts use Kibana to investigate alerts, drill down into specific events, and take appropriate actions (e.g., blocking IPs, disabling accounts).
User-Friendly: Simplifies querying compared to Elasticsearch's Query DSL.
Powerful: Supports advanced filtering, logical operators, wildcards, and regular expressions.
Real-Time Insights: Enables SOC analysts to quickly extract actionable insights from indexed data.
Field:Value Pairs:
Queries are composed of field:value pairs, where:
field represents the attribute of the data.
value specifies the data being searched for.
Example:
Filters data to show events with the Windows event code 4625, which corresponds to failed login attempts.
Allows searching for a term across all indexed fields without specifying a field name.
Example:
Returns records containing the string "svc-sql1" in any field.
AND, OR, and NOT enable combining multiple conditions for precise filtering.
Example:
Filters data to show failed login attempts (event.code:4625) where the account is disabled (SubStatus:0xC0000072).
Supported operators: =, >, >=, <, <=, !=.
Example:
Filters data to show failed login attempts that occurred between March 3rd, 2023, and March 6th, 2023.
Wildcards (*, ?) and regular expressions allow pattern-based searches.
Example:
Filters data to show failed login attempts targeting usernames starting with "admin", such as "admin", "administrator", or "admin123".
1. Detecting Brute-Force Attacks
Query:
Identifies multiple failed login attempts from the IP address 192.168.1.100.
2. Investigating Disabled Accounts
Query:
Detects failed login attempts against disabled accounts, which may indicate credential misuse.
3. Monitoring Administrator Account Activity
Query:
Identifies suspicious login attempts targeting administrator accounts.
4. Analyzing Time-Bound Events
Query:
Focuses on failed login attempts within a specific time range for forensic analysis.
Ease of Use: KQL simplifies complex queries, making it accessible even for less technical users.
Efficiency: Enables rapid querying and filtering of large datasets.
Flexibility: Supports a wide range of query types, from simple searches to advanced correlations.
Actionable Insights: Helps SOC analysts quickly identify and respond to security incidents.
The Elastic Stack is a powerful SIEM solution that empowers organizations to collect, analyze, and visualize security-related data. By leveraging Kibana Query Language (KQL), SOC analysts can efficiently query and filter data to detect and respond to security incidents. With its intuitive syntax and robust features, KQL is an indispensable tool for extracting actionable insights from Elasticsearch data.
By mastering KQL, SOC analysts can enhance their ability to:
Identify potential threats (e.g., brute-force attacks, disabled account misuse).
Investigate incidents with precision.
Create custom dashboards and visualizations for real-time monitoring.
