# Technical Document Extraction: NetFlow Dataset Generation Pipeline ## 1. Document Overview This image is a technical flow diagram illustrating the sequential process of transforming raw network traffic data into a labeled NetFlow dataset. The diagram utilizes a series of dark teal rounded rectangular blocks connected by directional arrows to indicate data flow and processing stages. ## 2. Component Isolation and Transcription The diagram is organized into a primary horizontal pipeline with two vertical input branches. ### A. Primary Horizontal Pipeline (Left to Right) This represents the core transformation stages of the data. 1. **PCAP files**: The initial input source containing raw packet capture data. 2. **nProbe**: The processing engine that ingests the raw files. 3. **NetFlow dataset (Unlabelled)**: The intermediate output consisting of flow records without categorical labels. 4. **Labelling process**: The functional stage where metadata or ground truth is applied to the records. 5. **Final NetFlow dataset (Labelled)**: The terminal output of the pipeline, ready for machine learning or analysis. ### B. Vertical Input Branches These blocks provide necessary parameters or reference data to the primary pipeline. * **Defined Features**: (Top-down input to *nProbe*) Specifies the specific attributes or metrics to be extracted from the PCAP files during the flow generation process. * **Ground Truth File**: (Bottom-up input to *Labelling process*) Provides the authoritative reference data used to assign correct labels to the unlabelled NetFlow records. ## 3. Process Flow and Logic Description The workflow follows a linear progression with specific injection points for configuration and validation data: 1. **Data Ingestion & Extraction**: The process begins with **PCAP files** being fed into **nProbe**. Simultaneously, **Defined Features** are provided to **nProbe** to dictate which network characteristics are captured. 2. **Flow Generation**: **nProbe** processes the raw packets based on the defined features to produce a **NetFlow dataset (Unlabelled)**. 3. **Data Annotation**: This unlabelled dataset enters the **Labelling process**. At this stage, a **Ground Truth File** is introduced. The system correlates the flow records with the ground truth data. 4. **Output**: The result of the labeling process is the **Final NetFlow dataset (Labelled)**, which contains both the network features and their corresponding classifications. ## 4. Summary of Textual Elements | Element Type | Exact Text Content | | :--- | :--- | | Input Block 1 | PCAP files | | Input Block 2 | Defined Features | | Processor Block 1 | nProbe | | Intermediate Output | NetFlow dataset (Unlabelled) | | Processor Block 2 | Labelling process | | Input Block 3 | Ground Truth File | | Final Output | Final NetFlow dataset (Labelled) |

# Technical Document Extraction: Network Flow Processing Workflow ## Diagram Description The image depicts a sequential data processing workflow for network traffic analysis. The flowchart consists of six interconnected components with directional arrows indicating data flow. All components are represented as dark blue rounded rectangles with white text labels. ## Component Analysis 1. **PCAP files** (Leftmost component) - Input source for the workflow - Contains raw network packet capture data 2. **nProbe** (Second component) - Network monitoring tool - Processes PCAP files using Defined Features - Output: Unlabelled NetFlow dataset 3. **Defined Features** (Top component) - Configuration parameters for nProbe - Positioned above nProbe with downward arrow connection 4. **NetFlow dataset (Unlabelled)** (Third component) - Intermediate output from nProbe - Contains processed network flow data without classification 5. **Labelling process** (Fourth component) - Classification stage - Receives input from: - Unlabelled NetFlow dataset - Ground Truth File (via bidirectional arrow) - Output: Final Labelled dataset 6. **Ground Truth File** (Bottom component) - Reference dataset for supervised learning - Connected bidirectionally to Labelling process 7. **Final NetFlow dataset (Labelled)** (Rightmost component) - End product of the workflow - Contains classified network flow data ## Flowchart Structure - Horizontal progression from left to right - Vertical connection between Ground Truth File and Labelling process - All components connected by single-direction arrows except: - Bidirectional connection between Ground Truth File and Labelling process ## Textual Content All components contain the following text labels: 1. "PCAP files" 2. "nProbe" 3. "Defined Features" 4. "NetFlow dataset (Unlabelled)" 5. "Labelling process" 6. "Ground Truth File" 7. "Final NetFlow dataset (Labelled)" ## Process Flow 1. PCAP files → nProbe (using Defined Features) 2. nProbe → Unlabelled NetFlow dataset 3. Unlabelled dataset + Ground Truth File → Labelling process 4. Labelling process → Final Labelled dataset ## Technical Notes - No numerical data or quantitative metrics present - All connections use standard flowchart arrow notation - Color scheme: Dark blue (#003366) background with white text - No legends, axis titles, or numerical scales present - No secondary languages detected ## Workflow Purpose This diagram illustrates a supervised machine learning pipeline for network traffic classification, where raw PCAP data is processed through nProbe, enriched with defined features, and then classified using ground truth references to produce a labeled dataset for further analysis.