Flagship Product

AI Protocol Discovery

Discover undocumented protocols in hours, not months. A 5-phase automated pipeline using statistical analysis, ML classification, grammar learning, parser generation, and adaptive learning.

89%+
Classification Accuracy
2-4 hrs
Discovery Time
50K
Messages/sec Throughput
150+
Protocol Types

The Protocol Problem

Organizations worldwide are blocked by undocumented legacy protocols. Over 60% of enterprise protocols have no documentation, the engineers who understood them have retired, and manual reverse engineering costs $500K-$2M per protocol taking 6-12 months.

Digital transformation initiatives stall when they hit protocol barriers. Critical integrations are delayed, security vulnerabilities go undetected in opaque protocol implementations, and compliance audits fail due to lack of protocol visibility.

60%+ Undocumented
Enterprise protocols lacking any documentation
$500K-$2M Per Protocol
Manual reverse engineering cost
6-12 Months
Traditional reverse engineering timeline

The 5-Phase Discovery Pipeline

From raw network traffic to production-ready parsers, fully automated.

1

Statistical Analysis

Entropy calculation, byte frequency distribution, pattern detection, binary vs. text classification

5-10 seconds
2

ML Classification

CNN feature extraction, BiLSTM sequence learning, multi-class classification with confidence scoring

10-20 seconds
3

Grammar Learning

PCFG inference with EM algorithm, rule extraction, transformer-based semantic learning

1-2 minutes
4

Parser Generation

Template-based parser generation, validation logic, performance optimization, live testing

30-60 seconds
5

Adaptive Learning

Error analysis feedback loop, grammar refinement, field detection improvement, model retraining

Continuous

Core Capabilities

Statistical Analysis Engine

Analyzes raw protocol traffic to extract fundamental characteristics including entropy profiles, byte frequency distributions, and structural patterns.

  • Entropy calculation (structured data at 4.2 bits/byte)
  • Pattern detection with repeating sequence identification
  • Binary vs. text format classification

Deep Learning Classification

Uses CNN + BiLSTM architectures for multi-class protocol classification with confidence scoring across 150+ known protocol families.

  • CNN feature extraction for spatial pattern recognition
  • BiLSTM sequence learning for temporal context
  • Confidence scoring for classification certainty

PCFG Grammar Learning

Automatically learns protocol grammars using Probabilistic Context-Free Grammars with Expectation-Maximization for probabilistic inference.

  • EM algorithm for probabilistic rule inference
  • Transformer models for semantic field learning
  • Continuous refinement from parsing errors

Automatic Parser Generation

Generates production-ready parsers from learned grammars with built-in validation logic and performance optimization for high-throughput environments.

  • 50,000+ messages/sec parser throughput
  • Multi-language template-based generation
  • Real-traffic validation testing

Technical Architecture

Core Components

Orchestrator
Protocol Discovery Orchestrator
Main coordination layer managing the 5-phase pipeline. 1,400+ lines of orchestration logic.
ML
Protocol Classifier
CNN + BiLSTM deep learning model for multi-class protocol classification with confidence scoring.
Grammar
Enhanced PCFG Inference
Probabilistic Context-Free Grammar inference engine with EM algorithm for rule extraction.
Parser
Dynamic Parser Generator
Template-based parser generation with validation, optimization, and real-traffic testing.

Ready to Discover Your Protocols?

Start analyzing undocumented protocols in minutes with the open-source AI Protocol Discovery engine.

Quick Start Guide Star on GitHub Enterprise Support