A Purple Team security project focused on automating network reconnaissance with Python and Nmap, improving scan readability through XML-to-HTML transformation, and integrating scan output into defensive monitoring and detection workflows.
Traditional network reconnaissance often relies on manual scans or static cron jobs, which limits repeatability, visibility, and defensive correlation.
The goal of this project was to design an automated scanning workflow that could support both offensive reconnaissance and defensive detection within a Purple Team context.
The solution needed to generate consistent, readable artifacts while preserving raw data for forensic analysis and monitoring.
I focused on treating reconnaissance as a repeatable, auditable process rather than a one-off activity.
Instead of running Nmap directly, I built Python-based automation layers to control scan execution, manage output, and improve readability.
Each scan was designed to produce both machine-readable and human-readable artifacts to support investigation, detection tuning, and reporting.
Developed Python scripts to automate Nmap execution with configurable targets, options, and output handling
Implemented preconfigured scan wrappers for rapid and repeatable reconnaissance
Added support for interface selection and source IP spoofing to simulate adversarial scanning behavior
Captured scan results in XML format to preserve full technical detail
Converted XML output into formatted HTML reports using XSLT for improved readability
Organized scan artifacts with timestamps to enable historical comparison and monitoring
This project emphasized understanding how reconnaissance activity appears from both attacker and defender perspectives.
Automated scans were correlated with defensive tooling such as Snort, UFW logs, Splunk, and Security Onion to observe detection behavior.
Readable scan reports improved analyst efficiency while retaining raw XML for deeper inspection.
The workflow reinforced the importance of visibility, repeatability, and traceability in security operations.
Successfully built an automated reconnaissance pipeline that produces consistent, auditable scan artifacts.
Improved the readability and usability of Nmap results without sacrificing technical detail.
Demonstrated how offensive automation can be leveraged to strengthen defensive detection and monitoring.
Delivered a practical foundation for future enhancements such as scan diffing, SIEM integration, and containerized execution.
This project is fully documented on GitHub, including notes, commits, and future ideas.
