StreamLens/README.md

StreamLens - Ethernet Traffic Analyzer

Advanced network traffic analyzer for pcap files and live streams with specialized protocol dissection for aviation and industrial networks. Features sigma-based outlier identification, real-time statistical analysis, and both TUI and modern GUI interfaces with interactive signal visualization.

Quick Start

# Install dependencies  
pip install scapy numpy matplotlib

# For GUI mode (optional but recommended):
pip install PySide6

# For macOS users - install tkinter support for TUI visualization:
brew install python-tk@3.13

# Launch modern GUI with interactive plots
python streamlens.py --gui --pcap file.pcap

# GUI mode only (then open file via File menu)
python streamlens.py --gui

# Analyze pcap file with TUI (flows sorted by largest sigma outliers)
python streamlens.py --pcap file.pcap

# Live capture with real-time statistics
python streamlens.py --live --interface eth0

# Console output with outlier reporting  
python streamlens.py --pcap file.pcap --no-tui

# Generate comprehensive outlier report
python streamlens.py --pcap file.pcap --report

# Get pcap file information
python streamlens.py --pcap file.pcap --info

# Adjust outlier threshold (default: 3.0 sigma)
python streamlens.py --pcap file.pcap --outlier-threshold 2.0

# With BPF filter for live capture
python streamlens.py --live --filter "port 319 or port 320"

Features

🖥️ Modern GUI Interface (New!)

• Professional Qt Interface: Cross-platform GUI built with PySide6
• Interactive Flow List: Sortable table showing flows with sigma deviations, protocols, and frame types
• Automatic Plot Rendering: Click any flow to instantly view signal plots (no button needed)
• Embedded Matplotlib Plots: Interactive signal visualization with zoom, pan, and navigation toolbar
• Background PCAP Loading: Progress bar with non-blocking file processing
• File Management: Open PCAP files via dialog or command line
• Smart Status Feedback: Color-coded status messages for different flow types and states
• Threading Safety: Proper Qt threading eliminates segmentation faults

Enhanced TUI Interface

• Three-Panel Layout: Flows list (top-left), flow details (top-right), timing visualization (bottom)
• Sigma-Based Flow Sorting: Flows automatically sorted by largest outlier sigma deviation
• Real-time Navigation: Arrow keys to navigate between flows with instant detail updates
• Protocol-aware Display: Shows detected protocols in flow list and details
• Smart Protocol Detection: Prioritizes specialized protocols (Chapter 10, PTP, IENA) over generic ones
• Detailed Outlier Analysis: Individual rows showing frame numbers and exact time deltas for outlier packets
• Visual Timeline: ASCII timeline showing frame timing deviations with outlier highlighting
• Live Statistics: Real-time running averages and outlier detection during capture

Core Analysis Engine

• Flow-based Analysis: Groups packets by source-destination IP pairs with timing statistics
• Configurable Outlier Detection: Adjustable sigma threshold (default: 3.0σ)
• Multi-layer Protocol Analysis: Ethernet, IP, UDP, TCP with specialized dissectors
• Real-time Statistical Updates: Running statistics for live capture mode
• High Jitter Flow Identification: Coefficient of variation analysis

Specialized Protocol Dissectors

• Chapter 10 (IRIG 106-17): Complete packet dissection including data types, timestamps, and payload analysis
• PTP (IEEE 1588-2019): Precision Time Protocol message parsing with sync, delay, and announce messages
• IENA (Airbus): Industrial Ethernet Network Architecture with P/D/N/M/Q message types

📊 Chapter 10 Signal Visualization

• Interactive GUI Plots: Select any flow to automatically view embedded matplotlib plots
• TUI Signal Plots: Press v in the TUI to generate signal files (threading-safe)
• Signal Consolidation: Automatically combines multiple packets from the same channel into continuous signals
• TMATS Integration: Automatically extracts channel metadata from TMATS frames for proper signal scaling
• Multi-channel Support: Displays multiple channels with proper engineering units and scaling
• Threading Safety: GUI uses proper Qt integration, TUI saves plots to files to avoid segfaults
• Both Modes: Works for both PCAP analysis and live capture
• Matplotlib Features: Full zoom, pan, save, and navigation capabilities

Protocol Detection & Fallbacks

• Automatic protocol identification based on port numbers and packet structure
• Fallback to common protocols: HTTP, HTTPS, SSH, DNS, DHCP, NTP, SNMP, IGMP, ICMP
• Multicast detection for aviation/industrial networks
• Enhanced error handling and validation

Installation

# Clone or download the project
cd streamlens

# Install dependencies  
pip install scapy numpy matplotlib PySide6

# Run the analyzer
python streamlens.py --help

Key Features Highlights

🎯 Sigma-Based Flow Prioritization

Flows are automatically sorted by their largest outlier sigma deviation, putting the most problematic flows at the top of the list for immediate attention.

📊 Real-time Statistics

Live capture mode provides running averages and outlier detection as packets arrive, with TUI updates every 500ms.

🔍 Configurable Analysis

Adjust outlier detection sensitivity with --outlier-threshold (default: 3.0σ) to fine-tune analysis for your specific network conditions.

📈 Comprehensive Reporting

Generate detailed outlier reports with --report flag showing frame-by-frame sigma deviations and timing analysis.

GUI Usage

Main Interface

• Left Panel: File information and flow list sorted by sigma deviation
• Right Panel: Interactive matplotlib plot area with navigation toolbar
• Status Bar: Loading progress and operation feedback

Workflow

1. Launch GUI: python streamlens.py --gui
2. Open PCAP: File → Open PCAP... or use command line --pcap flag
3. Select Flow: Click on any flow in the table to automatically view signal plots
4. Interact: Use matplotlib toolbar to zoom, pan, save plots
5. Navigate: Click different flows to instantly see their signal visualizations

TUI Controls

• ↑↓: Navigate between flows in main view
• v: Visualize Chapter 10 signals for selected flow (saves plot files)
• t: Toggle timeline panel on/off
• d: Switch to frame dissection view
• m or ESC: Return to main view
• q: Quit application

Timeline Visualization

The bottom panel displays a visual timeline of the selected flow's timing behavior:

• Horizontal axis: Progression through packet sequence
• Vertical axis: Deviation from average inter-arrival time (centered on average)
• Characters: · = normal timing, •/○ = moderate deviation, █/▄ = outliers
• Scale: Automatically adjusts to show full range of deviations
• Info bar: Shows total frames, deviation range, and outlier count

Project Structure

streamlens/
├── streamlens.py                    # Main entry point
├── analyzer/                        # Core analysis package
│   ├── main.py                     # CLI argument handling and main logic
│   ├── analysis/                   # Analysis engine
│   │   ├── core.py                # Main analyzer class
│   │   ├── flow_manager.py        # Flow tracking and management
│   │   └── statistics.py          # Statistical analysis and outlier detection
│   ├── models/                     # Data structures
│   │   ├── flow_stats.py          # Flow and frame type statistics
│   │   └── analysis_results.py    # Analysis result containers
│   ├── protocols/                  # Protocol dissectors
│   │   ├── base.py                # Base dissector interface
│   │   ├── chapter10.py           # IRIG106 telemetry protocol
│   │   ├── ptp.py                 # IEEE 1588 Precision Time Protocol
│   │   ├── iena.py                # Airbus IENA protocol
│   │   └── standard.py            # Standard protocol detection
│   ├── gui/                        # Modern GUI Interface (NEW!)
│   │   ├── __init__.py            # GUI package initialization
│   │   └── main_window.py         # PySide6 main window with matplotlib integration
│   ├── tui/                        # Text User Interface
│   │   ├── interface.py           # Main TUI controller
│   │   ├── navigation.py          # Navigation handling
│   │   └── panels/                # UI panel components
│   │       ├── flow_list.py       # Flow list panel
│   │       ├── detail_panel.py    # Flow details panel
│   │       └── timeline.py        # Timeline visualization panel
│   └── utils/                      # Utility modules
│       ├── pcap_loader.py         # PCAP file handling
│       ├── live_capture.py        # Live network capture
│       └── signal_visualizer.py   # Chapter 10 signal visualization (thread-safe)
└── *.pcapng                        # Sample capture files