Data Aggregation, Correlation, Normalization, and Querying

Info

This note summarizes core concepts in security data management: aggregation, normalization, correlation, and querying—including practical examples using Google Cloud tools like Chronicle.

🔄 Data Aggregation

Aggregation is the process of collecting and consolidating diverse forms of data from various sources (e.g., logs from firewalls, IDS/IPS, authentication systems).

Example

Analogy: Like a farmer harvesting vegetables from different fields and sending them to a central facility for processing, security systems collect logs from multiple components and aggregate them for analysis.

Google Cloud Chronicle acts as a SIEM tool that aggregates log data from different cloud infrastructure components to provide a unified security view.

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🧹 Data Normalization

Normalization ensures data is consistently formatted, making it easier to process and analyze. It enhances the performance of correlation by enabling uniform comparisons.

Tip

Normalized data allows security systems to match fields like timestamps, IP addresses, and usernames across different formats and sources.

Chronicle uses a Unified Data Model (UDM) for normalization, functioning like a structured filing cabinet where each data point is labeled and stored for easy retrieval.

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🔗 Data Correlation

Correlation involves identifying relationships between two or more security events by adding context and drawing logical connections.

Example

Analogy: A farmer linking low crop yields to drought conditions is similar to correlating a suspicious login with geolocation and time-based patterns.

Use Case:

• Suspicious Login Detection
  • Alert: New device login
  • Correlation: Compare IP address, login time, and historical behavior
  • Outcome: Legitimate if context matches user’s normal pattern

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🔍 Data Querying

Querying enables filtering and extracting meaningful information from vast logs. Common techniques include:

🔤 Regular Expressions (RegEx)

• Match specific patterns in text (e.g., SSH login failures)
• Syntax highlights:
  • ^ for start of string
  • . for any single character
  • [] for character ranges
  • \s for whitespace
  • \d{5} for five-digit numbers

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🔎 What is YARA-L?

YARA-L is a rule-based language used within Chronicle to create detection rules that scan large volumes of ingested log data. It functions as part of the Chronicle Detection Engine, enabling threat analysts to define complex event logic.

This reference applies to YARA-L version 2.0. Always use the current version to avoid compatibility issues with the Detection Engine.

✅ Best Practices for YARA-L

1. Filter Out Zero Values

When comparing fields that may be omitted in events, Chronicle assigns them default zero values. Directly comparing these fields can result in unintended matches.

Problematic: $e1.field1 = $e2.field2 (both may be omitted, equating to zero)

Recommended: $e1.field = "" — Ensures fields are explicitly empty, avoiding false matches.

2. Use Event-Type Filters

If your rule is intended to act only on UDM events, always specify an event-type filter. This narrows the evaluation scope and improves rule efficiency.

Filtering to UDM events reduces system overhead and minimizes irrelevant matches.

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🛠️ YARA-L and UDM Searches in Chronicle

• YARA-L: Detection rule language in Chronicle
  • Example: Detect user logins from more than one city in 5 minutes
  • Components: rule, meta, events, match, condition
• UDM Searches: Operate on normalized, structured logs using Chronicle’s UDM format

Note

Effective querying using tools like RegEx and YARA-L greatly reduces time needed for threat investigation and enhances detection accuracy.

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With mastery over aggregation, normalization, correlation, and querying, you’re equipped to extract actionable intelligence from complex datasets and respond swiftly to security incidents.

Quote

“Security is not a product, but a process.” — Bruce Schneier

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