Extracting Flow-Level Network Features from PCAPs with Tranalyzer2

Source: Dev.to

Why Flow‑Level Feature Extraction Matters

Flow‑level representation is a fundamental abstraction in modern network traffic analysis. Instead of operating on individual packets, flows summarize communication behavior between endpoints over time, enabling scalable analysis even for large PCAP datasets. Effective flow feature extraction is therefore a critical prerequisite for downstream tasks such as traffic characterization, anomaly detection, and machine‑learning‑based modeling.

Why Tranalyzer2?

Tranalyzer2 is designed specifically for high‑performance flow‑based traffic analysis. Unlike tools that either focus only on packet inspection or provide minimal NetFlow‑style statistics, Tranalyzer2 offers:

• Native flow construction from PCAPs
• Extensive protocol awareness (L2–L7 via plugins)
• Rich statistical, temporal, and behavioral features
• Modular plugin‑based architecture
• Structured outputs suitable for direct analytical use

Its ability to extract hundreds of flow‑level attributes in a single pass significantly reduces preprocessing overhead and simplifies large‑scale traffic‑analysis workflows.

Feature Categories Extracted by Tranalyzer2

Tranalyzer2 enables extraction of a wide spectrum of flow features covering multiple network dimensions. In this configuration, the extracted attributes span several categories, including but not limited to:

General flow attributes

• Flow direction, duration, packet counts, byte counts, and inter‑arrival metrics

Statistical flow features

• Minimum, maximum, average, variance, skewness, and kurtosis of packet sizes and inter‑arrival times

Connection and state features

• Flow state indicators, connection patterns, and bidirectional statistics

Transport‑layer features

• TCP flags, window sizes, retransmission indicators, and sequence behavior

Security‑relevant protocol features

• TLS/SSL handshake metadata, cipher information, version indicators, and fingerprints

Entropy and payload‑derived metrics

• Entropy ratios and payload distribution statistics useful for encrypted‑traffic characterization

Advanced timing and distribution features

• Packet‑timing dispersion, burstiness, and flow‑level behavioral signatures

Extracting Flow‑Level Features Using Tranalyzer2

Tranalyzer2 follows a plugin‑driven architecture, where flow‑level features are generated by selectively enabling plugins. Each plugin contributes a specific category of features (e.g., basic flow statistics, transport‑layer behavior, protocol metadata, entropy‑based metrics). Effective feature extraction therefore begins with careful plugin selection and configuration.

Step 1: Enable Required Tranalyzer2 Plugins

Before processing any PCAP files, activate the plugins that correspond to the desired feature categories. Typical plugins include:

• Core flow generation and statistical summaries
• Transport‑layer behavior and connection dynamics
• Security‑ and protocol‑related metadata (e.g., TLS attributes)
• Entropy and payload‑derived metrics
• Output sinks for structured data storage

In this workflow the mysqlSink plugin is enabled to store extracted flow records directly into a MySQL database, providing scalable storage, schema‑level control, and flexible downstream export. After selecting the required plugins, rebuild Tranalyzer2 so the enabled components are compiled into the processing pipeline.

Step 2: Process PCAP Files and Generate Flow Records

Once the plugins are enabled and Tranalyzer2 is rebuilt, process PCAP files via its command‑line interface. Process each PCAP independently to preserve flow integrity and ensure consistent feature extraction across captures.

Create separate directories for input data and results to keep the workflow organized:

mkdir ~/data ~/results

Process a PCAP file with the t2 command:

t2 -r ~/data/sample_traffic.pcap -w ~/results/

During this step:

• Packets are aggregated into bidirectional flows
• Plugin‑specific flow features are computed in real time
• Flow records are written directly into MySQL via the mysqlSink plugin

Note: Some statistical attributes (e.g., high‑precision timing, higher‑order moments) may require adjustments to the MySQL schema—such as increasing numeric precision for duration fields or modifying columns for skewness/kurtosis—to avoid insertion errors and ensure accurate storage.

Step 3: Export Flow‑Level Features to CSV

After the flow records are stored in MySQL, export them to CSV for further analysis. Log in to MySQL and verify the flow table contains all desired features. Rather than listing columns manually, you can export all flow‑level features with SELECT *:

# Export all flow records to a CSV file
mysql -u mysql -p -D tranalyzer -e "
SELECT *
FROM flow
" > ~/path/to/output.csv

The resulting CSV file can be loaded into pandas, R, or any analytics platform for downstream modeling, visualization, or anomaly detection.

With the flow‑level features exported to CSV, your data is now structured and ready for analysis, visualization, or machine‑learning pipelines. Using Tranalyzer2 in combination with MySQL makes traffic analysis modular, reproducible, and easy to integrate into downstream projects.

For more details and tutorials, check out the Tranalyzer2 Tutorials.