🤖 AI Features - PQ Crypta Documentation

🤖 AI-Powered Cryptographic Intelligence

PQ Crypta integrates artificial intelligence to enhance cryptographic operations, threat detection, and system optimization through Google Gemini, an advanced RAG chatbot, and ML-powered analysis.

🚀 Gemini 2.5 Flash - High-Speed LLM Inference

PQ Crypta integrates Google's Gemini 2.5 Flash for real-time natural language processing with a 1M-token context window and high free-tier throughput.

Gemini 2.5 Flash

High-performance, low-latency inference
Context window: 1M tokens
Strong reasoning with optional thinking mode
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

// Gemini 2.5 Flash Configuration
const geminiConfig = {
    model: 'gemini-2.5-flash',
    apiKey: process.env.GEMINI_API_KEY,
    maxOutputTokens: 2048,
    temperature: 0.7,
    thinkingBudget: 0,
    streamingEnabled: true
};

// Example usage
const stream = geminiClient.models.generateContentStream({
    model: 'gemini-2.5-flash',
    contents: conversationHistory,
    config: geminiConfig
});
                

🧙 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: 'gemini',
    model: 'gemini-2.5-flash',
    contextWindow: 1048576,

    // 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

// 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 /ml/recommend-algorithm

Real algorithm recommendation from algorithm_selection.py's AlgorithmSelectionSystem: a rule-based scorer (security/performance/compatibility/compliance/maturity, weighted) plus a neural network trained via knowledge distillation against that same scorer (95.1% validation accuracy). confidence_score is the network's real softmax output, not a fixed number. /ml/select-algorithm returns the identical result shape.

{
  "use_case": "high_security_iot_sensor",
  "security_requirements": ["quantum-safe", "critical"],
  "performance_constraints": {
    "priority": "high",
    "data_size_range": [256, 4096],
    "frequency": "high"
  }
}

// Response (live, unedited)
{
  "success": true,
  "data": {
    "recommended_algorithm": "hybrid",
    "confidence_score": 0.4279499351978302,
    "match_score": 91.0,
    "alternative_algorithms": [["post-quantum", 87.0], ["multi-algorithm", 84.5], ["classical", 71.75]],
    "selection_rationale": [
      "Excellent security match - meets QUANTUM_SAFE requirements",
      "Provides quantum resistance as required"
    ],
    "optimization_suggestions": ["Enable hardware acceleration for improved performance"],
    "performance_prediction": {
      "key_generation_ms": 45.3, "encryption_ms_per_mb": 15.2,
      "decryption_ms_per_mb": 12.8, "memory_usage_mb": 64.0,
      "ciphertext_overhead_percent": 12.5
    },
    "security_analysis": {
      "quantum_resistant": true, "security_level": 5,
      "side_channel_resistance": 4, "threat_coverage": ["quantum"]
    },
    "compliance_status": {},
    "use_case": "high_security_iot_sensor"
  }
}
                        

🌐 Federated Learning

Live: POST /ai/federated-threat-model. Real FedAvg (and 6 other) aggregation with Gaussian-mechanism differential privacy (federated_learning.py's ModelAggregator / DifferentialPrivacy, called via ml_bridge.rs::federated_threat_detection). Each submitted data sample is treated as one client's update; DP noise is applied, the real aggregation strategy computes the genuine aggregate, and each client's actual deviation from that aggregate is the threat/anomaly signal. Note: the "distributed clients" are simulated by varying one feature vector in a single process, not real multi-node computation - the aggregation and privacy math itself is real.

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 network (Qiskit 1.4.5 + PennyLane 0.42.2) actually running in production - it drives the padding-size optimization inside the entropy-orchestrated algorithm engine (anti-traffic-analysis, not confidentiality/integrity). Verified live: encrypting with that algorithm logs a real PennyLane circuit execution (Hybrid QNN: 5→64→4Q→64→2).

Qiskit 1.4.5

IBM Quantum (live via the VQE endpoint below; not used by the QNN itself, which runs on PennyLane)

PennyLane 0.42.2

Live in production (entropy-orchestrated padding)

Quantum Architecture

Hybrid Quantum-Classical Network (entropy-orchestrated padding prediction)
PyTorch integration for the classical encode/decode layers

Quantum Features

Real per-qubit PauliZ expectation values from the live circuit
Entanglement measure computed from those values (1 - mean(|<Z>|)), varies per call

🔬 Variational Quantum Eigensolver (VQE)

Live: POST /quantum/vqe. Real Qiskit-based ground-state energy estimation via qiskit_algorithms. Verified live for H2 at 0.735Å bond length, converging to a real ground-state energy.

VQE Capabilities

Molecular Hamiltonian (H2)
Max-Cut Problem Solving
Traveling Salesman Problem

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

🤖 AI Features Documentation

🤖 AI-Powered Cryptographic Intelligence

🚀 Gemini 2.5 Flash - High-Speed LLM Inference

Gemini 2.5 Flash

Integration Features

Use Cases

🧙 The Wizard - Advanced RAG Chatbot

Vector Search (FAISS)

Hybrid Search Pipeline

Advanced Features

Query Processing

Hybrid Retrieval

Re-ranking

Generation

🔐 Zero-Knowledge Proof Systems

Proof Systems

Verification Modes

🛡️ AI Threat Assessment

Data Collection

Pattern Analysis

Risk Scoring

Response

Anomaly Detection

Attack Classification

Predictive Analysis

🔍 AI Vulnerability Detection

Static Code Analysis

Dynamic Analysis

Configuration Review

📈 Intelligent Performance Optimization

Performance Prediction

Optimization Recommendations

Adaptive Intelligence

🌐 AI/ML API Endpoints

POST /api/ai/analyze-threat

POST /api/ml/predict-performance

POST /api/ai/detect-vulnerabilities

POST /ml/recommend-algorithm

🌐 Federated Learning

Aggregation Strategies

Privacy Mechanisms

⚛️ Quantum Neural Networks

Quantum Architecture

Quantum Features

🔬 Variational Quantum Eigensolver (VQE)

VQE Capabilities

Optimizers

🔐 Fully Homomorphic Encryption (FHE)

FHE Operations

Security Features

Authentication

Rate Limits