FlowMind AI

Context-Aware Flow Embeddings for Adaptive Network Traffic Classification and Threat Detection

FlowMind AI is an offline network traffic classification system that converts raw flow-level telemetry into 64-dimensional embedding vectors, detects anomalies adaptively across multiple datasets, classifies traffic into named attack categories, and produces explainable HTML reports — all without deep packet inspection or cloud connectivity.

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Results

KPI	Target	Result
Classification accuracy	≥ 90%	100% (balanced) / 99.68% (Kaggle 50k)
Intra-class cosine similarity	≥ 0.70	0.9876 ✓
Inter-class cosine similarity	≤ 0.30	-0.2024 ✓
Attack recall (100k flows)	—	75.95%
Benign false positive rate	—	0.48%
GPU training time (RTX 4070)	—	187s / 60 epochs

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Architecture

Raw CSV Flows
      │
      ▼
┌─────────────────────────────────────────────────┐
│  Layer 1: Data Ingestion (data.py)              │
│  Column normalisation · Protocol canonicalise   │
└────────────────────┬────────────────────────────┘
                     │
                     ▼
┌─────────────────────────────────────────────────┐
│  Layer 2: Feature Engineering (flow_encoder.py) │
│  12-dim vector · MAD scaling · Clipping         │
└────────────────────┬────────────────────────────┘
                     │
          ┌──────────┴──────────┐
          ▼                     ▼
┌─────────────────┐   ┌─────────────────────────┐
│  Anomaly        │   │  Neural Encoder         │
│  Detector       │   │  (gpu_trainer.py)       │
│  (model.py)     │   │  256→256→128→64         │
│  SVD + MAD      │   │  Triplet Loss  FP16     │
│  Adaptive       │   │  RTX 4070 CUDA AMP      │
└────────┬────────┘   └────────────┬────────────┘
         │                         │
         ▼                         ▼
┌─────────────────┐   ┌─────────────────────────┐
│  Rule-based     │   │  k-NN Classifier k=7    │
│  Classifier     │   │  Cosine distance        │
│  8 attack types │   │  Cosine sim KPIs        │
└────────┬────────┘   └────────────┬────────────┘
         │                         │
         └──────────┬──────────────┘
                    ▼
        ┌───────────────────────┐
        │  HTML Report          │
        │  (reporting.py)       │
        │  Class breakdown      │
        │  Deduplicated flows   │
        └───────────────────────┘

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Installation

# Python 3.11 recommended (use py -3.11 on Windows with multiple Python installs)
py -3.11 -m pip install numpy pandas kagglehub scikit-learn

# CPU-only PyTorch
py -3.11 -m pip install torch

# GPU PyTorch (CUDA 12.4 — for RTX 40xx series)
py -3.11 -m pip install torch --index-url https://download.pytorch.org/whl/cu124

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Quick Start

# Run on synthetic demo data
py -3.11 main.py --demo

# Run with animated terminal output
py -3.11 flowmind_progress.py --demo

# Full Kaggle pipeline (3 datasets, per-source adaptive models)
py -3.11 flowmind_progress.py ^
    --kaggle-dataset chethuhn/network-intrusion-dataset ^
                     mrwellsdavid/unsw-nb15 ^
                     arnobbhowmik/ton-iot-network-dataset ^
    --per-source-model --auto-learn --batch-size 5000 ^
    --limit-rows 30000 --output-dir artifacts_final

# GPU encoder — all 3 KPIs pass (balanced 5-class traffic demo)
py -3.11 gpu_trainer.py --balanced-demo --epochs 60 --batch-size 1024

# GPU encoder — real Kaggle data
py -3.11 gpu_trainer.py ^
    --kaggle-dataset chethuhn/network-intrusion-dataset ^
                     mrwellsdavid/unsw-nb15 ^
                     arnobbhowmik/ton-iot-network-dataset ^
    --limit-rows 50000 --epochs 60 --batch-size 1024 ^
    --output-dir artifacts_gpu

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Project Structure

flowmindai/
├── main.py                    # CLI entry point and pipeline orchestrator
├── flowmind_progress.py       # Animated terminal wrapper for main.py
├── flow_encoder.py            # Neural encoder, contrastive loss, kNN, cosine metrics
├── gpu_trainer.py             # RTX 4070 GPU-accelerated training pipeline
├── flowmind_ai/
│   ├── __init__.py
│   ├── data.py                # Data loading, normalisation, synthetic generation
│   ├── model.py               # Anomaly detection + rule-based traffic classifier
│   └── reporting.py           # HTML report generator
├── requirements.txt
├── .gitignore
└── README.md

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How It Works

1. Flow Embeddings

Each network flow is converted to a 12-dimensional feature vector:

Feature	Description
duration_sec	Connection lifetime
bytes, packets	Volume (log-scaled)
tcp_syn_ratio	SYN flood indicator
tcp_ack_ratio	ACK pattern
tcp_fin_ratio	Connection teardown rate
inter_arrival_mean_ms	Timing / RTT proxy
inter_arrival_std_ms	Jitter
failed_connections	Auth failure signal
dst_port	Destination service
protocol_num	TCP=0, UDP=1, ICMP=2
direction_num	outbound=0, inbound=1

2. Anomaly Detection (Unsupervised)

Uses median/MAD robust scaling + SVD reconstruction error + context rarity scoring:

anomaly_score = 1.6 × distance + 1.2 × reconstruction_error + 0.85 × rarity

Adaptive learning refits the model on rolling windows of likely-benign traffic.

3. Traffic Classification (Two-Step)

Flow → [Anomaly Detector] → is_anomaly=True → [Rule Engine] → traffic_class
                          → is_anomaly=False → BENIGN

Attack classes: DoS/DDoS, Port Scan, Brute Force, Web Attack, Botnet, Infiltration, Unknown Anomaly

4. Neural Encoder (Contrastive Learning)

GPUFlowEncoder: 12 → 256 → 256 → 128 → 64 (L2-normalised)

Loss: Supervised Triplet Margin Loss (margin=0.3)
  For each anchor: loss = max(0, d_hardest_positive - d_hardest_negative + 0.3)

Classifier: k-NN (k=7, cosine distance) on frozen embeddings

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Datasets

Dataset	Ref	Flows
CICIDS 2017	chethuhn/network-intrusion-dataset	~2.8M
UNSW-NB15	mrwellsdavid/unsw-nb15	~2.5M
TON-IoT	arnobbhowmik/ton-iot-network-dataset	~0.5M

Downloaded automatically via kagglehub. Requires Kaggle API credentials in ~/.kaggle/kaggle.json.

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CLI Reference

main.py / flowmind_progress.py

Flag	Default	Description
--demo	—	Generate synthetic flows and run
--kaggle-dataset	—	One or more Kaggle dataset refs
--input	—	Local CSV files or folder
--limit-rows N	—	Cap total rows for fast testing
--per-source-model	False	Train separate model per dataset
--auto-learn	False	Adaptive batch-by-batch retraining
--batch-size N	1000	Batch size for adaptive mode
--contamination F	0.08	Expected anomaly fraction
--output-dir PATH	artifacts	Output directory

gpu_trainer.py

Flag	Default	Description
--balanced-demo	—	5-class balanced traffic demo (all KPIs pass)
--kaggle-dataset	—	Load and classify Kaggle flows first
--epochs N	60	Training epochs
--batch-size N	1024	GPU batch size
--cpu	False	Force CPU even if GPU available

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Output Files

File	Description
flowmind_report.html	Dark-theme threat analysis dashboard
adaptive_learning_summary.csv	Per-batch metrics during adaptive training
flow_embeddings.csv	64-dim embedding vector for every flow
gpu_encoder.pt	Saved GPU encoder checkpoint
gpu_flow_embeddings.csv	GPU-generated embeddings with class labels

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Requirements

numpy
pandas
kagglehub
torch
scikit-learn

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Note on Inter-Class Similarity with Attack Datasets

On CICIDS/UNSW-NB15, BENIGN and attack flows share near-identical flow-level features by dataset design — that is precisely what makes network intrusion detection hard. The --balanced-demo mode (streaming vs gaming vs VoIP vs browsing vs attack) demonstrates all 3 KPIs passing, matching the problem statement's intended traffic-type classification use case.