1. EXECUTIVE SUMMARY

This document provides guidance on Python proficiency for personnel supporting telecommunications metadata analysis and anomaly detection tasks. Focus areas include scalable data engineering, network traffic parsing, and foundational machine learning techniques applicable to unclassified and FOUO datasets.

2. RECOMMENDED PYTHON MODULES

2.1 Core Data Handling

• Pandas/NumPy: Metadata aggregation (e.g., call detail records, IP traffic logs).

• PySpark: Batch processing of terabyte-scale datasets on Apache Hadoop clusters.

• Dask: Parallel task scheduling for multi-threaded workflows.

2.2 Network Analysis

• Scapy: Basic packet crafting and analysis for network forensics training.

• dpkt: PCAP file parsing to identify protocol anomalies (e.g., malformed DNS queries).

2.3 Machine Learning

• Scikit-learn: Clustering and classification of benign/malicious IP traffic patterns.

• TensorFlow Lite: Lightweight anomaly detection models for edge-device deployment.

3. USE CASES (FOUO)

3.1 Metadata Pattern Recognition

Scenario: Identifying statistical outliers in cellular tower connection logs (e.g., burner phone detection).
Tools: Pandas (time-series resampling), Seaborn (visualization of geospatial clusters).

3.2 Encrypted Traffic Analysis

Scenario: Profiling TLS handshake behavior to flag non-compliant VPN usage.
Tools: Scapy (JARM fingerprinting), Scikit-learn (supervised classification).

3.3 Log Correlation

Scenario: Cross-referencing firewall logs with DNS queries to detect low-level C2 activity.
Tools: PySpark (join operations on distributed datasets).

4. TRAINING PATHWAYS

4.1 Agency Resources

• Python for Data Analysis (PY101): Instructor-led course covering Pandas/NumPy for log analysis.

• Network Forensics Primer (NET200): Scapy labs simulating basic CNE scenarios.

4.2 External Upskilling (Approved)

• Coursera: „Applied Data Science with Python“ (University of Michigan).

• HTB Academy: „Traffic Analysis“ module for PCAP investigation drills.

5. SECURITY PROTOCOLS

• Data Handling: FOUO datasets must be processed on NSANet-approved virtual environments.

• Tool Licensing: Open-source libraries require validation via NSA Code Review Board (CRB).

• Output Sanitization: All visualizations/reports must redact PII per DoD 5400.11-R.