TrustIdentity.AI 1.0.1

TrustIdentity.AI

AI-powered fraud detection and behavioral analysis

📦 Overview

TrustIdentity.AI provides AI and machine learning capabilities for fraud detection, behavioral analysis, and adaptive authentication. This is a unique feature not available in Duende IdentityServer.

✨ Features

• ✅ Real-time Fraud Detection - ML-based anomaly detection
• ✅ Behavioral Analysis - User behavior profiling
• ✅ Risk Scoring - Composite risk calculation
• ✅ Adaptive Authentication - AI-driven MFA triggers
• ✅ Device Fingerprinting - Track user devices
• ✅ Anomaly Detection - Unusual access patterns

🚀 Installation

dotnet add package TrustIdentity.AI
dotnet add package TrustIdentity.ML  # Optional: ML.NET integration

🔧 Usage

Basic Setup

using TrustIdentity.AspNetCore.Extensions;

builder.Services.AddTrustIdentity(options =>
{
    options.IssuerUri = "https://localhost:5001";
    options.EnableAI = true;
    options.EnableFraudDetection = true;
})
.AddAIFraudDetection()
.AddBehaviorAnalysis()
.AddRiskScoring();

Advanced Configuration

builder.Services.AddTrustIdentity(options =>
{
    options.EnableAI = true;
    options.EnableFraudDetection = true;
    
    // AI Configuration
    options.AIOptions = new AIOptions
    {
        FraudDetectionThreshold = 0.7,
        EnableBehavioralAnalysis = true,
        EnableDeviceFingerprinting = true,
        EnableAnomalyDetection = true,
        RiskScoreThreshold = 0.8
    };
});

🧠 AI Services

IFraudDetectionService

Detects fraudulent login attempts in real-time.

public interface IFraudDetectionService
{
    Task<FraudDetectionResult> AnalyzeLoginAttemptAsync(LoginAttempt attempt);
    Task<bool> IsSuspiciousAsync(string userId, string ipAddress);
}

Usage:

public class LoginController
{
    private readonly IFraudDetectionService _fraudDetection;

    public async Task<IActionResult> Login(LoginModel model)
    {
        var attempt = new LoginAttempt
        {
            UserId = model.Username,
            IpAddress = HttpContext.Connection.RemoteIpAddress?.ToString(),
            UserAgent = Request.Headers["User-Agent"],
            Timestamp = DateTime.UtcNow
        };

        var result = await _fraudDetection.AnalyzeLoginAttemptAsync(attempt);

        if (result.IsFraudulent)
        {
            // Block login or require additional verification
            return Forbid("Suspicious activity detected");
        }

        // Continue with normal login
    }
}

IBehaviorAnalysisService

Analyzes user behavior patterns.

public interface IBehaviorAnalysisService
{
    Task<BehaviorProfile> GetUserProfileAsync(string userId);
    Task UpdateBehaviorAsync(string userId, UserActivity activity);
    Task<bool> IsAnomalousAsync(string userId, UserActivity activity);
}

Usage:

var profile = await _behaviorAnalysis.GetUserProfileAsync(userId);

var activity = new UserActivity
{
    UserId = userId,
    IpAddress = ipAddress,
    Location = location,
    DeviceId = deviceId,
    Timestamp = DateTime.UtcNow
};

if (await _behaviorAnalysis.IsAnomalousAsync(userId, activity))
{
    // Trigger MFA or additional verification
}

Risk Scoring

Calculate composite risk scores:

var riskScore = await _riskScoring.CalculateRiskScoreAsync(new RiskContext
{
    UserId = userId,
    IpAddress = ipAddress,
    DeviceId = deviceId,
    Location = location,
    TimeOfDay = DateTime.UtcNow.TimeOfDay
});

if (riskScore > 0.8)
{
    // High risk - require MFA
}
else if (riskScore > 0.5)
{
    // Medium risk - additional verification
}
else
{
    // Low risk - allow login
}

🎯 Use Cases

1. Adaptive MFA

Trigger MFA based on risk score:

var riskScore = await _riskScoring.CalculateRiskScoreAsync(context);

if (riskScore > 0.7)
{
    // Require MFA
    return RedirectToAction("MFA");
}

2. Fraud Prevention

Block suspicious login attempts:

var fraudResult = await _fraudDetection.AnalyzeLoginAttemptAsync(attempt);

if (fraudResult.IsFraudulent)
{
    await _logger.LogSecurityEventAsync("Fraudulent login blocked", userId);
    return Forbid();
}

3. Device Tracking

Track and verify user devices:

var deviceId = await _deviceFingerprinting.GetDeviceIdAsync(request);
var isKnownDevice = await _deviceTracking.IsKnownDeviceAsync(userId, deviceId);

if (!isKnownDevice)
{
    // New device - send verification email
    await _emailService.SendNewDeviceNotificationAsync(userId, deviceId);
}

📊 AI Models

Fraud Detection Model

• Algorithm: Isolation Forest
• Features: IP address, location, time of day, device, user agent
• Training: Continuous learning from login patterns

Behavioral Analysis Model

• Algorithm: LSTM (Long Short-Term Memory)
• Features: Login times, locations, devices, access patterns
• Training: Per-user behavior profiling

Risk Scoring Model

• Algorithm: Ensemble (Random Forest + Gradient Boosting)
• Features: Composite of fraud and behavior scores
• Training: Supervised learning on labeled data

🔧 Configuration

appsettings.json

{
  "TrustIdentity": {
    "AI": {
      "EnableFraudDetection": true,
      "EnableBehavioralAnalysis": true,
      "EnableRiskScoring": true,
      "FraudDetectionThreshold": 0.7,
      "RiskScoreThreshold": 0.8,
      "ModelUpdateInterval": 3600,
      "EnableDeviceFingerprinting": true
    }
  }
}

🏗️ Architecture

TrustIdentity.AI/
├── Analyzers/          # AI analyzers
│   ├── FraudDetectionService.cs
│   ├── BehaviorAnalysisService.cs
│   └── RiskScoringService.cs
├── Models/            # ML models
├── Services/          # AI services
└── Extensions/        # Configuration extensions

📚 Documentation

• Setup Guide - General setup
• Main Documentation - Overview

📄 License

Apache 2.0 - See LICENSE