Crosschain Attack Detection using GAN

A novel deep learning approach for detecting bridge attacks in cross-chain blockchain transactions using Conditional Generative Adversarial Networks (cGAN) with anomaly-based detection.

Overview

CCAD-GAN is a deep learning-based anomaly detection system designed to identify malicious transactions in cross-chain blockchain bridges. The system leverages a two-stage GAN training approach to learn normal transaction patterns and detect attacks through reconstruction error analysis.

Key Features

• 93.2% Detection Accuracy on cross-chain bridge attacks
• Two-Stage Training: Pretraining (Autoencoder) + GAN Training
• Real-time Detection with low latency (<50ms per transaction)
• 4×4 Grid Matrix Encoding for transaction representation
• Anomaly-based Detection using reconstruction error thresholding
• Production-Ready deployment architecture

Supported Attack Types

• 🔴 Replay Attacks - Transaction replay detection
• 🔴 Double-Spend Attacks - Duplicate spending identification
• 🔴 Signature Forgery - Invalid signature detection
• 🔴 Manipulation Attacks - Transaction data tampering

System Architecture

1. Data Collection Layer - Monitors Ethereum, BSC, Polygon, and bridge APIs
2. Preprocessing Module - Converts transactions to 4×4 grid matrices (128×128 pixels)
3. CCAD-GAN Model - Encoder-Generator-Discriminator architecture
4. Detection Engine - Calculates reconstruction errors and classifies transactions
5. Alert System - Triggers security alerts for detected attacks

Dataset Structure

Each cross-chain transaction is represented as a 4×4 grid (16 features).

Feature Grid

Matrix Generation

Each 4×4 raw feature matrix is expanded into a 128×128 grayscale image. Values are normalized into the range [0, 1] using:

M_ij = ( f_ij − min(f) ) / ( max(f) − min(f) )

Dataset Statistics

• Valid Transactions: 2,000 samples
• Attack Transactions: 400 samples (4 attack types × 100 each)
• Training/Test Split: 80% training, 20% testing
• Image Dimensions: 128 × 128 × 1 (grayscale)

Model Architecture

The CCAD-GAN consists of three interconnected neural networks trained in two stages.

1. Encoder

The Encoder compresses the 128×128 input transaction matrix into a 256-dimensional latent vector.

Total Parameters: ~5.2M

2. Generator

The Generator reconstructs the original 128×128 transaction matrix from the 256-dimensional latent vector.

3. Discriminator

The Discriminator classifies transactions as real or fake (reconstructed).

Algorithm & Methodology

Two-Stage Training Approach

Stage 1: Encoder Pretraining

Objective: Learn to reconstruct valid transactions.

# ALGORITHM
1. Initialize Encoder E and Generator G  
2. FOR epoch = 1 to 15:  
     FOR each batch of valid transactions x:  
         z = E(x)  
         x_reconstructed = G(z)  
         loss = MSE(x, x_reconstructed)  
         Update E and G using Adam optimizer  
     IF loss < 0.005 -> BREAK  
3. Save pretrained weights

# Hyperparameters
- Batch size: 16  
- Learning rate: 0.0002  
- Optimizer: Adam (β₁ = 0.5, β₂ = 0.999)  
- Epochs: 15

Stage 2: GAN Training

Objective: Improve reconstruction quality and add adversarial learning.

# ALGORITHM
1. Load pretrained E and G  
2. Initialize Discriminator D  
3. FOR epoch = 1 to 20:  
     • Train Discriminator:  
         z = E(x)  
         x_fake = G(z)  
         loss_D = BCE(D(x), 1) + BCE(D(x_fake), 0)  
         Update D  
     • Train Generator:  
         z = E(x)  
         x_fake = G(z)  
         loss_G = 10 · MSE(x, x_fake) + BCE(D(x_fake), 1)  
         Update E and G  
     IF D_loss ∈ [0.3, 0.7] for 3 consecutive epochs -> BREAK  

Results & Performance

Training Progress

Detection Performance

• ✅ Accuracy: 93.2%
• ✅ Precision: 99.1%
• ✅ Recall: 87.2%
• ✅ F1-Score: 92.8%
• ✅ AUC-ROC: 0.929

Project Structure

ccad-gan/
├── data/
│   ├── valid_transactions/          # Valid transaction matrices
│   └── attack_transactions/         # Attack transaction matrices
├── models/
│   ├── ccad_gan_final.pth           # Trained model weights
│   └── ccad_pretrained_autoencoder.pth
├── Diagrams/
│   ├── Flowchart.jpeg
│   ├── System_Architecture.png
│   └── ...
├── Results/
│   ├── ccad_gan_training_losses.png
│   └── ...
├── CCAD_GAN.ipynb                  # Training notebook
├── ccad_gan.py
├── README.md
└── LICENSE

Future Work

• Multi-chain support (Solana, Avalanche, Arbitrum)
• Real-time streaming detection
• Explainable AI for attack attribution
• Integration with blockchain monitoring platforms
• Zero-knowledge proof validation
• Federated learning for privacy-preserving training