This article explains how we conduct comprehensive cyber threat data analysis using Google Cloud, from data extraction and preprocessing to trend analysis and presentation. It emphasizes the value of BigQuery, Python, and Google Sheets - showcasing how to refine and visualize data for insightful cybersecurity analysis.

Data extraction tools and techniques

BigQuery for data staging and querying

Colab Notebooks for loading staged datasets

Essential Python libraries for data analysis

Organizing Colab Notebooks before analysis

Extracting Our BigQuery datasets into dataframes

Embracing the art of data cleaning

Utilizing Pandas for data cleaning

Feature extraction and enrichment

Feature extraction

Data enrichment

Normalization

Anomaly detection: Refining the process of data analysis

Statistical methods – The backbone of analysis:

Aggregations and sorting – unraveling layers:

Visualization – The lens of clarity:

Machine learning – The advanced guard:

Beginning with a broad overview: Aggregation and sorting

The power of time: Time series analysis

Correlation analysis

Machine learning & anomaly detection

Behavioral patterns & endpoint data analysis

Selecting and aggregating key data points

Exporting reduced data to Google Sheets for accessibility

Pivot tables for in-depth analysis

Journey through Google's Cloud ecosystem

Modularity and flexibility in workflow

Orchestration and tool synchronization

Navigating the seas of cyber threat data with Google Cloud

Google Cloud for Cyber Data Analytics

ES|QL is Elastic's new piped query language. Taking full advantage of this new feature, Elastic Security Labs walks through how to run validation of ES|QL rules for the Detection Engine.

Author

Eric Forte

Articles

Google Cloud for Cyber Data Analytics

Streamlining ES|QL Query and Rule Validation: Integrating with GitHub CI