🤖 AI-Powered Cryptographic Intelligence
PQ Crypta integrates artificial intelligence to enhance cryptographic operations, threat detection, and system optimization through Groq API, advanced RAG chatbot, and ML-powered analysis.
🚀 Groq API - High-Speed LLM Inference
PQ Crypta integrates Groq's ultra-fast inference API for real-time natural language processing with exceptional performance and low latency.
Llama 3.3 70B Versatile
- Ultra-fast inference (750+ tokens/second)
- Context window: 128K tokens
- Advanced reasoning capabilities
- Real-time response generation
Integration Features
- Streaming responses with SSE
- Conversation history management
- Temperature and parameter control
- Automatic fallback mechanisms
Use Cases
- Interactive chatbot conversations
- Cryptographic documentation queries
- Real-time code analysis
- Security policy generation
// Groq API Configuration
const groqConfig = {
model: 'llama-3.3-70b-versatile',
apiKey: process.env.GROQ_API_KEY,
maxTokens: 2000,
temperature: 0.7,
streamingEnabled: true
};
// Example usage
const response = await groqClient.chat.completions.create({
model: 'llama-3.3-70b-versatile',
messages: conversationHistory,
stream: true
});
🧙 The Wizard - Advanced RAG Chatbot
An advanced Retrieval-Augmented Generation (RAG) chatbot with comprehensive codebase knowledge and intelligent query processing.
37,760
Indexed Chunks
FAISS
Vector Store
Hybrid
Search Strategy
Cross-Enc
Re-ranking
Vector Search (FAISS)
- 37,760 code chunks indexed
- 384-dimensional embeddings
- all-MiniLM-L6-v2 model
- Semantic similarity search
Hybrid Search Pipeline
- Dense retrieval (FAISS)
- Sparse retrieval (BM25)
- Cross-encoder re-ranking
- Context-aware filtering
Advanced Features
- Query decomposition
- Multi-hop reasoning
- Conversation memory (15 messages)
- Source citation and verification
// RAG Pipeline Configuration
const ragConfig = {
// Vector store
vectorStore: 'faiss',
indexedChunks: 37760,
embeddingModel: 'all-MiniLM-L6-v2',
embeddingDimension: 384,
// Hybrid search
hybridSearch: {
denseWeight: 0.7,
sparseWeight: 0.3,
reranker: 'cross-encoder',
topK: 5
},
// LLM integration
llmBackend: 'groq',
model: 'llama-3.3-70b-versatile',
contextWindow: 8000,
// Advanced features
queryDecomposition: true,
multiHopReasoning: true,
conversationMemory: 15
};
1
Query Processing
Decomposition & expansion
2
Hybrid Retrieval
Dense + Sparse search
3
Re-ranking
Cross-encoder scoring
4
Generation
Context-aware response
🔐 Zero-Knowledge Proof Systems
Production-grade zero-knowledge proof implementation using arkworks cryptographic libraries for privacy-preserving verification.
6
ZK Systems
arkworks
Library
Groth16
Primary
BN254
Curve
Proof Systems
- Groth16 - smallest proofs, fast verification
- PLONK - universal SNARKs, no trusted setup per-circuit
- Bulletproofs - constant-time friendly, no trusted setup
- STARK - quantum-resistant, transparent
- Nova - recursive proofs, IVC
- Halo2 - no trusted setup, efficient
Verification Modes
- Fast - development/testing (skip verification)
- Secure - production (full verification)
- Audit - compliance (verification + detailed logging)
- Custom - user-defined behavior
Blockchain Integration
- Smart contract deployment for on-chain verification
- Gas optimization with batch verification
- Multi-blockchain support (Ethereum, Polygon, etc.)
- Proof aggregation for efficiency
// Zero-Knowledge Proof Implementation (Rust + arkworks)
// Located in: /var/www/html/public/ent/core/src/zk_verification.rs
use ark_bn254::{Bn254, Fr as Bn254Fr};
use ark_groth16::{Groth16, PreparedVerifyingKey, Proof, ProvingKey};
use ark_relations::r1cs::{ConstraintSynthesizer, ConstraintSystemRef};
pub trait ZkVerifiable {
fn generate_zk_proof(
&self,
operation_data: &[u8],
context: &HashMap<String, String>,
) -> Result<ZkProofContainer, ZkVerificationError>;
fn verify_zk_proof(
&self,
proof: &ZkProofContainer,
) -> Result<bool, ZkVerificationError>;
}
// Blockchain integration with multi-network support
pub struct ZKProofSystem {
proof_systems: Vec<ZKProofSystemType>, // Groth16, PLONK, etc.
blockchain_networks: Vec<BlockchainNetwork>,
gas_optimization: GasOptimizationConfig,
}
🛡️ AI Threat Assessment
Intelligent threat detection and risk assessment using advanced machine learning models.
1
Data Collection
Real-time monitoring
2
Pattern Analysis
Anomaly detection
3
Risk Scoring
Threat classification
4
Response
Automated mitigation
Anomaly Detection
- Network traffic analysis
- Behavioral pattern recognition
- Statistical outlier detection
- Temporal anomaly identification
Attack Classification
- SQL injection detection
- XSS attack identification
- Command injection analysis
- Cryptographic weakness scanning
Predictive Analysis
- Threat trend prediction
- Attack vector forecasting
- Security incident prediction
- Risk escalation modeling
🔍 AI Vulnerability Detection
Automated vulnerability scanning and code analysis using AI-powered detection engines.
Static Code Analysis
- Cryptographic implementation review
- Hardcoded secret detection
- Insecure algorithm identification
- Code quality assessment
Dynamic Analysis
- Runtime vulnerability detection
- Memory safety analysis
- Protocol implementation testing
- Side-channel attack detection
Configuration Review
- Security configuration analysis
- Best practice compliance
- Parameter validation
- Automated remediation suggestions
// Vulnerability Detection Example
const threatPatterns = {
'sql-injection': {
severity: 'high',
pattern: /(\bUNION\b|\bSELECT\b|\bDROP\b)/i
},
'crypto-weakness': {
severity: 'high',
pattern: /\b(MD5|SHA1|DES|RC4|ECB)\b/i
},
'hardcoded-secret': {
severity: 'critical',
pattern: /(password|secret|key)\s*[:=]\s*['"][^'"]{8,}/i
}
};
📈 Intelligent Performance Optimization
AI-driven performance analysis and optimization recommendations for cryptographic operations.
Performance Prediction
- Algorithm performance forecasting
- Resource usage prediction
- Scalability analysis
- Bottleneck identification
Optimization Recommendations
- Algorithm selection guidance
- Parameter tuning suggestions
- Hardware utilization optimization
- Caching strategy recommendations
Adaptive Intelligence
- Real-time performance monitoring
- Automatic parameter adjustment
- Load balancing optimization
- Self-healing system capabilities
🌐 AI/ML API Endpoints
RESTful API endpoints for integrating AI and ML capabilities into external applications.
POST /api/ai/analyze-threat
Analyze potential security threats using AI models.
{
"data": "suspicious network activity log",
"context": {
"source": "external",
"authenticated": false,
"timeOfDay": 23
}
}
// Response
{
"threatLevel": "high",
"confidence": 0.85,
"anomalies": [...],
"attackPatterns": [...],
"riskScore": 0.76
}
POST /api/ml/predict-performance
Predict cryptographic algorithm performance using ML models.
{
"algorithm": "ML-KEM-1024",
"dataSize": 1048576,
"systemSpecs": {
"cpu": "x64",
"memory": 8192,
"cores": 4
}
}
// Response
{
"prediction": {
"throughput": 1250,
"latency": 0.8,
"confidence": 0.94
}
}
POST /api/ai/detect-vulnerabilities
Scan code or configuration for security vulnerabilities.
{
"code": "password = 'hardcoded123'",
"type": "javascript"
}
// Response
{
"vulnerabilities": [
{
"id": "hardcoded-secret",
"severity": "critical",
"description": "Hardcoded secret detected",
"location": 0,
"evidence": "password = 'hardcoded123'"
}
],
"riskScore": 0.9
}
POST /api/ml/recommend-algorithm
Get intelligent algorithm recommendations based on requirements.
{
"requirements": {
"securityLevel": "high",
"performanceRequired": true,
"dataType": "large-files",
"quantumThreat": true
}
}
// Response
{
"recommendation": {
"algorithm": "ML-KEM-1024",
"confidence": 0.96,
"reasoning": "High security with quantum resistance",
"alternatives": ["hybrid", "multi-pq"]
}
}
🌐 Federated Learning
Privacy-preserving distributed machine learning with secure multi-party computation.
7
Aggregation Strategies
DP
Differential Privacy
Byzantine
Robust
Aggregation Strategies
- FedAvg - Federated Averaging
- FedProx - Proximal Term Regularization
- FedYogi - Adaptive Yogi Optimizer
- FedAdam - Adaptive Momentum
- Krum - Byzantine-Robust
- Median - Coordinate-wise Median
- Trimmed Mean - Outlier Removal
Privacy Mechanisms
- Differential Privacy (Gaussian Mechanism)
- Secure Multi-Party Computation
- Gradient Clipping
- Noise Addition
- Secret Sharing
⚛️ Quantum Neural Networks
Hybrid quantum-classical neural networks using Qiskit 2.1.1 and PennyLane 0.42.2.
Qiskit 2.1.1
IBM Quantum
PennyLane 0.42.2
Lightning Backend
Quantum Architectures
- Variational Quantum Circuits (VQC)
- Quantum Convolutional Layers
- Hybrid Quantum-Classical Networks
- Quantum Kernel Methods
- Quantum GANs
Quantum Features
- Real Quantum Hardware Support
- Circuit Transpilation
- Error Mitigation
- Quantum Transfer Learning
- PyTorch Integration
🔬 Variational Quantum Eigensolver (VQE)
Quantum algorithm for ground state energy calculation and optimization problems.
VQE Capabilities
- Molecular Hamiltonian (H2, etc.)
- Max-Cut Problem Solving
- TSP Optimization
- Adaptive VQE
- Quantum Natural Gradients
Optimizers
- SPSA - Simultaneous Perturbation
- COBYLA - Constrained Optimization
- L-BFGS-B - Limited Memory BFGS
- SLSQP - Sequential Least Squares
🔐 Fully Homomorphic Encryption (FHE)
RLWE-based homomorphic encryption for privacy-preserving computation on encrypted data.
RLWE + AES-256-GCM
Lattice-Based
Boolean + Integer
Operations
FHE Operations
- Boolean Circuit Evaluation
- 8/16/32/64-bit Integer Arithmetic
- Homomorphic Addition
- Homomorphic Multiplication
- Privacy-Preserving Computation
Security Features
- 128-bit Security Level
- Key Serialization
- Client-Server Architecture
- No Data Decryption Required
Authentication
All AI/ML API endpoints require authentication using API keys.
// Headers required for all requests
{
"Authorization": "Bearer your-api-key-here",
"Content-Type": "application/json"
}
Rate Limits
API rate limits ensure fair usage and system stability.
- AI Analysis: 100 requests/hour
- ML Predictions: 200 requests/hour
- Vulnerability Scans: 50 requests/hour
- Algorithm Recommendations: 100 requests/hour