# AI-Driven Vulnerability Detection

### Overview

Glitch Gremlin uses machine learning to enhance its vulnerability detection capabilities. The ML model analyzes both static and dynamic program features to identify potential security issues.

### Features

#### Static Analysis

* Code pattern recognition
* Control flow analysis
* State variable tracking
* Cross-program invocation detection

#### Dynamic Analysis

* Transaction trace analysis
* Memory access patterns
* Instruction sequence modeling
* Error pattern detection

### Model Architecture

The vulnerability detection model uses a deep neural network with:

* Input layer: Dense (128 units, ReLU activation) for processing 20 program features
* Regularization: Dropout layer (0.2) to prevent overfitting
* Hidden layer: Dense (32 units, ReLU activation) for pattern recognition
* Output layer: Dense with softmax activation for multi-class vulnerability prediction

Key features:

* Input features include transaction patterns, memory access, error rates
* Confidence scoring for each prediction
* Pattern analysis for detailed vulnerability insights
* Model persistence with save/load capabilities

### Usage

```typescript
import { GlitchSDK, TestType } from '@glitch-gremlin/sdk';

const sdk = new GlitchSDK({
    cluster: 'devnet',
    wallet: yourWallet
});

// Create request with ML configuration
const request = await sdk.createChaosRequest({
    targetProgram: "Your program ID",
    testType: TestType.EXPLOIT,
    duration: 300,
    intensity: 7,
    params: {
        mlConfig: {
            confidenceThreshold: 0.8,
            featureExtraction: {
                includeStaticAnalysis: true,
                includeDynamicTraces: true
            }
        }
    }
});

// Get results with ML predictions
const results = await request.waitForCompletion();
console.log('ML Predictions:', results.mlPredictions);
```

### Model Training

The model is trained on:

* Known vulnerability patterns
* Historical exploit data
* Community-submitted test cases
* Synthetic program traces

### Confidence Scores

ML predictions include confidence scores (0-1):

* 0.9-1.0: Very high confidence
* 0.7-0.9: High confidence
* 0.5-0.7: Medium confidence
* <0.5: Low confidence

### Best Practices

1. Start with high confidence thresholds (0.8+)
2. Enable both static and dynamic analysis
3. Review ML predictions alongside traditional test results
4. Contribute validated findings back to the training dataset

### Next Steps

* Customize ML Parameters
* Contribute Training Data
* ML Model Architecture