Design Authentication and Fraud Detection for Roblox - System Design Interview

Introduction

Designing authentication and fraud detection systems for Roblox is a critical security challenge that requires handling millions of daily active users, protecting against sophisticated attacks, detecting fraudulent activities in real-time, and maintaining a seamless user experience. The system must handle account creation, login, session management, multi-factor authentication, bot detection, payment fraud, content fraud, and real-time threat analysis.

This post provides a detailed walkthrough of designing authentication and fraud detection systems for Roblox, covering key architectural decisions, security patterns, fraud detection algorithms, real-time analysis, and scalability considerations. This is a common system design interview question that tests your understanding of distributed systems, security, authentication, fraud detection, machine learning, and real-time processing.

Problem Statement
Requirements
- Functional Requirements
- Non-Functional Requirements
Capacity Estimation
- Traffic Estimates
- Storage Estimates
Core Entities
API
Data Flow
Database Design
- Schema Design
- Database Sharding Strategy
High-Level Design
Deep Dive
What Interviewers Look For
Summary

Problem Statement

Design authentication and fraud detection systems for Roblox that:

Handle user registration and login securely
Manage user sessions and tokens
Support multi-factor authentication (2FA)
Detect and prevent bot accounts
Detect payment fraud and unauthorized transactions
Detect content fraud (stolen assets, inappropriate content)
Analyze user behavior for suspicious patterns
Provide real-time threat detection and response
Scale to millions of users and billions of events

Requirements

Functional Requirements

Authentication:

User registration with email/username and password
User login with credentials
Password reset via email
Session management (create, validate, refresh, revoke)
Multi-factor authentication (2FA) via SMS/email/app
OAuth integration (Google, Apple, etc.)
Account recovery
Device management (trusted devices)

Fraud Detection:

Bot detection (automated account creation, gameplay)
Payment fraud detection (stolen cards, chargebacks)
Content fraud detection (stolen assets, copyright violations)
Account takeover detection
Suspicious behavior detection (rapid actions, unusual patterns)
IP reputation analysis
Device fingerprinting
Real-time risk scoring

Security:

Rate limiting on authentication endpoints
CAPTCHA for suspicious activities
Account lockout after failed attempts
Security alerts and notifications
Audit logging of security events

Non-Functional Requirements

Performance:

Authentication latency: < 200ms (p95)
Fraud detection latency: < 500ms (p95)
Support 100K+ login requests/second
Support 1M+ fraud checks/second

Scalability:

Handle 100M+ registered users
Handle 50M+ daily active users
Handle 1B+ authentication events/day
Handle 10B+ fraud detection events/day

Reliability:

99.99% availability
Zero data loss for authentication events
Real-time fraud detection with < 1s delay

Security:

Encrypt passwords (bcrypt/argon2)
Secure token storage and transmission
Rate limiting to prevent brute force
DDoS protection
Compliance (GDPR, COPPA for children)

Capacity Estimation

Traffic Estimates

Authentication Traffic:

Daily active users: 50M
Login attempts per user per day: 2
Total login requests: 100M/day = ~1,200/sec (peak: 5,000/sec)
Registration requests: 500K/day = ~6/sec (peak: 50/sec)
Password reset requests: 2M/day = ~25/sec (peak: 200/sec)
2FA requests: 10M/day = ~120/sec (peak: 1,000/sec)

Fraud Detection Traffic:

Authentication events: 100M/day = ~1,200/sec
Payment events: 5M/day = ~60/sec
Content upload events: 10M/day = ~120/sec
Gameplay events: 1B/day = ~12,000/sec
Total fraud checks: ~15,000/sec (peak: 50,000/sec)

Storage Estimates

User Data:

Users: 100M
User profile: 2KB/user = 200GB
Authentication data: 1KB/user = 100GB
Total: ~300GB

Session Data:

Active sessions: 50M
Session data: 500 bytes/session = 25GB
TTL: 7 days
Total with replication: ~50GB

Fraud Detection Data:

Event logs: 10B events/day × 1KB = 10TB/day
Retention: 90 days = 900TB
ML model data: 100GB
Total: ~1PB

Audit Logs:

Security events: 1B/day × 500 bytes = 500GB/day
Retention: 365 days = 180TB

Core Entities

User:

user_id (UUID)
username
email
password_hash
phone_number (optional)
created_at
last_login_at
account_status (active, suspended, banned)
security_settings

Session:

session_id (UUID)
user_id
device_id
ip_address
user_agent
created_at
expires_at
refresh_token
is_active

Authentication Event:

event_id (UUID)
user_id
event_type (login, logout, registration, password_reset)
ip_address
device_id
user_agent
success
failure_reason
timestamp
risk_score

Fraud Event:

fraud_event_id (UUID)
user_id
event_type (bot_detected, payment_fraud, content_fraud, account_takeover)
risk_score
confidence
detected_at
action_taken (blocked, flagged, allowed)
features (JSON)

Device:

device_id (UUID)
user_id
device_fingerprint
device_type
os_version
browser_version
is_trusted
first_seen_at
last_seen_at

Risk Score:

risk_id (UUID)
user_id
event_type
risk_score (0-100)
factors (JSON)
timestamp

API

Authentication APIs

POST /api/v1/auth/register

Request:
{
  "username": "player123",
  "email": "player@example.com",
  "password": "SecurePass123!",
  "captcha_token": "token"
}

Response:
{
  "user_id": "uuid",
  "session_token": "jwt_token",
  "expires_at": "2025-12-06T00:00:00Z"
}

POST /api/v1/auth/login

Request:
{
  "username": "player123",
  "password": "SecurePass123!",
  "device_id": "device_uuid",
  "captcha_token": "token" // if suspicious
}

Response:
{
  "session_token": "jwt_token",
  "refresh_token": "refresh_token",
  "expires_at": "2025-12-06T00:00:00Z",
  "requires_2fa": false
}

POST /api/v1/auth/2fa/verify

Request:
{
  "session_token": "jwt_token",
  "code": "123456"
}

Response:
{
  "session_token": "jwt_token",
  "expires_at": "2025-12-06T00:00:00Z"
}

POST /api/v1/auth/logout

Request:
{
  "session_token": "jwt_token"
}

Response:
{
  "success": true
}

POST /api/v1/auth/password/reset

Request:
{
  "email": "player@example.com"
}

Response:
{
  "success": true,
  "message": "Reset link sent to email"
}

GET /api/v1/auth/session/validate

Request:
Headers: {
  "Authorization": "Bearer jwt_token"
}

Response:
{
  "valid": true,
  "user_id": "uuid",
  "expires_at": "2025-12-06T00:00:00Z"
}

Fraud Detection APIs

POST /api/v1/fraud/check

Request:
{
  "user_id": "uuid",
  "event_type": "login",
  "ip_address": "192.168.1.1",
  "device_id": "device_uuid",
  "features": {
    "login_velocity": 5,
    "device_age": 30,
    "payment_amount": 0
  }
}

Response:
{
  "risk_score": 25,
  "action": "allow",
  "requires_captcha": false,
  "requires_2fa": false
}

GET /api/v1/fraud/user/{user_id}/risk

Response:
{
  "user_id": "uuid",
  "current_risk_score": 45,
  "risk_factors": [
    "unusual_login_location",
    "rapid_payment_attempts"
  ],
  "recommended_action": "flag_for_review"
}

Data Flow

Authentication Flow

User Registration:
- User submits registration form
- System validates input (email format, password strength)
- CAPTCHA verification
- Check if email/username exists
- Hash password (bcrypt/argon2)
- Create user record
- Generate session token
- Send verification email
- Log authentication event
- Return session token
User Login:
- User submits credentials
- System validates credentials
- Check account status (not banned/suspended)
- Fraud detection check (risk scoring)
- If high risk: require CAPTCHA or 2FA
- Create session
- Generate JWT token
- Log authentication event
- Return session token
Session Validation:
- Client sends request with JWT token
- API Gateway validates token signature
- Check token expiration
- Verify session in cache (Redis)
- If valid: allow request
- If expired: return 401, client refreshes token
Password Reset:
- User requests password reset
- System generates secure reset token
- Store token with expiration (15 minutes)
- Send reset link via email
- User clicks link, enters new password
- System validates token and updates password
- Invalidate all existing sessions

Fraud Detection Flow

Event Collection:
- System collects events (login, payment, content upload)
- Extract features (IP, device, behavior patterns)
- Send to fraud detection service
Real-Time Analysis:
- Fraud detection service receives event
- Extract user features (historical behavior)
- Calculate risk score using ML models
- Check against rules engine
- Determine action (allow, block, flag, require_2fa)
Response:
- Return risk score and action
- If high risk: trigger additional security (CAPTCHA, 2FA)
- If fraud detected: block action, log fraud event
- Update user risk profile
Batch Analysis:
- Periodically analyze historical events
- Update ML models
- Detect patterns and anomalies
- Generate reports

Database Design

Schema Design

users:

CREATE TABLE users (
    user_id UUID PRIMARY KEY,
    username VARCHAR(50) UNIQUE NOT NULL,
    email VARCHAR(255) UNIQUE NOT NULL,
    password_hash VARCHAR(255) NOT NULL,
    phone_number VARCHAR(20),
    account_status VARCHAR(20) NOT NULL, -- active, suspended, banned
    created_at TIMESTAMP NOT NULL,
    last_login_at TIMESTAMP,
    failed_login_attempts INT DEFAULT 0,
    locked_until TIMESTAMP,
    security_settings JSONB,
    INDEX idx_email (email),
    INDEX idx_username (username),
    INDEX idx_account_status (account_status)
);

sessions:

CREATE TABLE sessions (
    session_id UUID PRIMARY KEY,
    user_id UUID NOT NULL,
    device_id UUID,
    ip_address INET,
    user_agent TEXT,
    created_at TIMESTAMP NOT NULL,
    expires_at TIMESTAMP NOT NULL,
    refresh_token VARCHAR(255),
    is_active BOOLEAN DEFAULT true,
    FOREIGN KEY (user_id) REFERENCES users(user_id),
    INDEX idx_user_id (user_id),
    INDEX idx_expires_at (expires_at),
    INDEX idx_refresh_token (refresh_token)
);

authentication_events:

CREATE TABLE authentication_events (
    event_id UUID PRIMARY KEY,
    user_id UUID,
    event_type VARCHAR(50) NOT NULL, -- login, logout, registration, password_reset
    ip_address INET,
    device_id UUID,
    user_agent TEXT,
    success BOOLEAN NOT NULL,
    failure_reason VARCHAR(100),
    risk_score INT,
    timestamp TIMESTAMP NOT NULL,
    INDEX idx_user_id (user_id),
    INDEX idx_timestamp (timestamp),
    INDEX idx_event_type (event_type),
    INDEX idx_risk_score (risk_score)
) PARTITION BY RANGE (timestamp);

fraud_events:

CREATE TABLE fraud_events (
    fraud_event_id UUID PRIMARY KEY,
    user_id UUID,
    event_type VARCHAR(50) NOT NULL, -- bot_detected, payment_fraud, content_fraud
    risk_score INT NOT NULL,
    confidence DECIMAL(5,2),
    detected_at TIMESTAMP NOT NULL,
    action_taken VARCHAR(50), -- blocked, flagged, allowed
    features JSONB,
    INDEX idx_user_id (user_id),
    INDEX idx_detected_at (detected_at),
    INDEX idx_event_type (event_type),
    INDEX idx_risk_score (risk_score)
) PARTITION BY RANGE (detected_at);

devices:

CREATE TABLE devices (
    device_id UUID PRIMARY KEY,
    user_id UUID NOT NULL,
    device_fingerprint VARCHAR(255) NOT NULL,
    device_type VARCHAR(50),
    os_version VARCHAR(50),
    browser_version VARCHAR(50),
    is_trusted BOOLEAN DEFAULT false,
    first_seen_at TIMESTAMP NOT NULL,
    last_seen_at TIMESTAMP NOT NULL,
    FOREIGN KEY (user_id) REFERENCES users(user_id),
    INDEX idx_user_id (user_id),
    INDEX idx_device_fingerprint (device_fingerprint)
);

risk_scores:

CREATE TABLE risk_scores (
    risk_id UUID PRIMARY KEY,
    user_id UUID NOT NULL,
    event_type VARCHAR(50) NOT NULL,
    risk_score INT NOT NULL, -- 0-100
    factors JSONB,
    timestamp TIMESTAMP NOT NULL,
    INDEX idx_user_id (user_id),
    INDEX idx_timestamp (timestamp),
    INDEX idx_risk_score (risk_score)
) PARTITION BY RANGE (timestamp);

Database Sharding Strategy

Sharding by user_id:

Shard key: user_id
Number of shards: 100
Shard function: hash(user_id) % 100
Enables efficient user data lookup

Time-based partitioning:

Partition authentication_events and fraud_events by timestamp
Monthly partitions
Enables efficient time-range queries
Easy archival of old data

High-Level Design

┌─────────────┐
│   Clients   │
│  (Web/Mobile)│
└──────┬──────┘
       │
       ▼
┌─────────────────────────────────────┐
│        API Gateway / Load Balancer   │
│        (Rate Limiting, SSL/TLS)      │
└──────┬───────────────────────────────┘
       │
       ├─────────────────────────────────┐
       │                                 │
       ▼                                 ▼
┌──────────────────┐         ┌──────────────────────┐
│  Authentication  │         │  Fraud Detection     │
│     Service       │         │     Service          │
│                   │         │                      │
│ - Registration    │         │ - Risk Scoring      │
│ - Login           │         │ - Bot Detection      │
│ - Session Mgmt    │         │ - Payment Fraud      │
│ - Password Reset  │         │ - Content Fraud     │
│ - 2FA             │         │ - ML Models          │
└──────┬────────────┘         └──────┬───────────────┘
       │                              │
       ├──────────────┬───────────────┤
       │              │               │
       ▼              ▼               ▼
┌──────────┐  ┌──────────┐  ┌──────────────┐
│  Redis   │  │PostgreSQL│  │   Kafka      │
│ (Sessions│  │  (Users, │  │  (Events)    │
│  Cache)  │  │  Events) │  │              │
└──────────┘  └──────────┘  └──────┬───────┘
                                    │
                                    ▼
                           ┌─────────────────┐
                           │  ML Pipeline    │
                           │  (Training,     │
                           │   Inference)    │
                           └─────────────────┘

Deep Dive

Authentication System

Password Hashing:

Use bcrypt or Argon2 (memory-hard function)
Cost factor: 12-14 (balance security vs performance)
Salt per password (stored with hash)
Never store plaintext passwords

JWT Tokens:

Access token: Short-lived (15 minutes), contains user_id, permissions
Refresh token: Long-lived (7 days), stored securely, used to get new access tokens
Signed with RS256 (asymmetric) or HS256 (symmetric)
Include: user_id, device_id, issued_at, expires_at

Session Management:

Store active sessions in Redis (fast lookup)
Key: session:{session_id}, Value: user_id, device_id, expires_at
TTL: 7 days (matches refresh token)
On logout: delete from Redis, invalidate refresh token

Rate Limiting:

Login attempts: 5 per minute per IP
Registration: 3 per hour per IP
Password reset: 3 per hour per email
Use Redis with sliding window or token bucket

Session Management

Session Creation:

User authenticates successfully
Generate session_id (UUID)
Create JWT access token (15 min TTL)
Create refresh token (7 day TTL, stored in DB)
Store session in Redis: session:{session_id} → user_id, device_id
Return tokens to client

Session Validation:

Client sends request with JWT token
API Gateway validates JWT signature
Check token expiration
Lookup session in Redis
If valid: allow request, optionally refresh token
If invalid/expired: return 401

Token Refresh:

Client sends refresh token
Validate refresh token (check DB, not expired)
Generate new access token
Optionally rotate refresh token
Return new tokens

Session Revocation:

On logout: delete from Redis, mark refresh token as revoked
On password change: invalidate all sessions
On suspicious activity: revoke all sessions, force re-login

Multi-Factor Authentication

2FA Methods:

SMS: Send 6-digit code via SMS (Twilio)
Email: Send code via email
TOTP: Time-based one-time password (Google Authenticator)
Push notification: Send push to trusted device

2FA Flow:

User logs in with credentials
System checks if 2FA enabled
If enabled: generate code, send via chosen method
Return session token with requires_2fa: true
User submits code
System validates code (check expiration, rate limit)
If valid: activate session, return full access token
If invalid: increment failed attempts, lock after 5 failures

Trusted Devices:

After successful 2FA, mark device as trusted
Trusted devices skip 2FA for 30 days
User can manage trusted devices in settings

Account Security

Account Lockout:

After 5 failed login attempts: lock account for 15 minutes
After 10 failed attempts: lock for 1 hour
After 20 failed attempts: require password reset
Store lockout info in Redis: lockout:{user_id} → locked_until

Password Requirements:

Minimum 8 characters
Require uppercase, lowercase, number, special character
Check against common password list
Prevent password reuse (last 5 passwords)

Security Alerts:

Email on new device login
Email on password change
Email on suspicious activity
Push notification for critical events

Device Management:

Track devices per user
Device fingerprinting (browser, OS, screen resolution)
Show active sessions in account settings
Allow user to revoke sessions

Fraud Detection System

Architecture:

Real-time scoring service (low latency)
Batch analysis service (deep analysis)
ML model serving (TensorFlow Serving, PyTorch)
Rules engine (business rules, thresholds)

Risk Scoring:

Score range: 0-100
0-30: Low risk (allow)
31-60: Medium risk (flag, require CAPTCHA/2FA)
61-80: High risk (block, manual review)
81-100: Critical risk (immediate block, alert)

Features for Risk Scoring:

IP reputation (known VPN, proxy, datacenter)
Device fingerprint (new device, suspicious device)
Behavioral patterns (login velocity, action patterns)
Account age and history
Geographic patterns (unusual location)
Payment history (chargebacks, refunds)
Content patterns (stolen assets, violations)

Bot Detection

Signals:

Registration velocity (many accounts from same IP)
Login patterns (too fast, too consistent)
Gameplay patterns (perfect timing, inhuman precision)
Device fingerprint (same device, many accounts)
CAPTCHA solving patterns (too fast, too accurate)
Network patterns (same subnet, many accounts)

Detection Methods:

Behavioral analysis (mouse movements, keystroke timing)
CAPTCHA challenges (invisible CAPTCHA, progressive challenges)
Device fingerprinting (browser, canvas, WebGL)
ML models (anomaly detection, classification)
Rate limiting (actions per second, requests per minute)

Response:

Low confidence: Flag for review
Medium confidence: Require CAPTCHA, rate limit
High confidence: Block account, require verification
Critical: Immediate ban, IP block

Payment Fraud Detection

Signals:

Card testing (many small transactions)
Chargeback history
Unusual payment patterns (large amounts, rapid purchases)
Geographic mismatch (card country vs user location)
Velocity checks (too many transactions in short time)
BIN analysis (card issuer, country)

Detection Methods:

Real-time scoring (ML models on payment features)
Rules engine (velocity limits, amount limits)
External services (Stripe Radar, Sift)
Historical analysis (user payment patterns)

Response:

Low risk: Allow transaction
Medium risk: Require additional verification (CVV, 3DS)
High risk: Block transaction, flag account
Critical: Block transaction, freeze account, alert

Content Fraud Detection

Signals:

Stolen assets (image hash matching, copyright detection)
Inappropriate content (ML image/video classification)
Spam content (text analysis, pattern matching)
Duplicate content (hash comparison)
Copyright violations (DMCA detection)

Detection Methods:

Image hashing (perceptual hashing for similar images)
ML models (content classification, NSFW detection)
Text analysis (spam detection, keyword filtering)
External services (Google Vision API, AWS Rekognition)
Manual review queue (flagged content)

Response:

Low risk: Allow, flag for review
Medium risk: Flag for manual review
High risk: Block upload, warn user
Critical: Block upload, suspend account

Real-Time Threat Analysis

Event Stream Processing:

Kafka for event streaming
Real-time processing (Kafka Streams, Flink)
Windowed aggregations (sliding window, tumbling window)
Pattern detection (CEP - Complex Event Processing)

Anomaly Detection:

Statistical methods (Z-score, IQR)
ML models (isolation forest, autoencoders)
Time series analysis (trend detection, seasonality)
Clustering (identify unusual clusters)

Threat Intelligence:

IP reputation databases
Known bot networks
Malware signatures
Threat feeds (abuse.ch, VirusTotal)

Response Pipeline:

Real-time alerts (PagerDuty, Slack)
Automated actions (block, rate limit, require verification)
Manual review queue (high-risk cases)
Incident response (investigation, remediation)

Machine Learning Models

Model Types:

Classification (fraud vs legitimate)
Regression (risk score prediction)
Anomaly detection (unusual patterns)
Clustering (user segments)

Features:

User features (account age, history, reputation)
Behavioral features (login patterns, action velocity)
Device features (fingerprint, OS, browser)
Network features (IP, geolocation, ASN)
Transaction features (amount, frequency, history)

Model Training:

Training data: Historical events with labels
Feature engineering: Extract features from raw events
Model selection: XGBoost, Random Forest, Neural Networks
Validation: Cross-validation, holdout set
A/B testing: Compare model performance

Model Serving:

Real-time inference (TensorFlow Serving, PyTorch)
Batch inference (Spark, Flink)
Model versioning (rollback capability)
Monitoring (prediction distribution, drift detection)

Model Updates:

Retrain periodically (daily, weekly)
Online learning (update with new data)
Feedback loop (use labeled fraud events)

Security Considerations

Data Protection:

Encrypt passwords (bcrypt/argon2)
Encrypt sensitive data at rest (AES-256)
Encrypt data in transit (TLS 1.3)
Tokenize payment information
PII anonymization for analytics

Access Control:

Role-based access control (RBAC)
Principle of least privilege
API authentication (API keys, OAuth)
Audit logging of admin actions

DDoS Protection:

Rate limiting (per IP, per user)
CAPTCHA for suspicious traffic
IP whitelisting/blacklisting
CDN for static content
WAF (Web Application Firewall)

Compliance:

GDPR: Right to deletion, data portability
COPPA: Special handling for children’s accounts
PCI DSS: Secure payment processing
SOC 2: Security controls and auditing

Trade-offs and Optimizations

Latency vs Accuracy:

Real-time scoring: Fast but less accurate
Batch analysis: Slower but more accurate
Solution: Hybrid approach (real-time + batch)

False Positives vs False Negatives:

Strict rules: Fewer false negatives, more false positives
Loose rules: More false negatives, fewer false positives
Solution: Tune thresholds based on business impact

Storage vs Performance:

Store all events: High storage, good for analysis
Store samples: Lower storage, may miss patterns
Solution: Store all events, archive old data

Privacy vs Security:

More data: Better fraud detection, privacy concerns
Less data: Privacy-friendly, worse detection
Solution: Collect necessary data, anonymize for analytics

What Interviewers Look For

Authentication & Security Knowledge

Password Security
- Proper hashing (bcrypt, Argon2)
- Password requirements and validation
- Password reset flow
- Red Flags: Plaintext passwords, weak hashing, no rate limiting
Session Management
- JWT vs session cookies
- Token refresh mechanism
- Session revocation
- Red Flags: No expiration, no revocation, insecure storage
Multi-Factor Authentication
- 2FA methods and trade-offs
- Trusted devices
- Backup codes
- Red Flags: No 2FA, weak 2FA, no backup

Fraud Detection Skills

Detection Methods
- Rule-based vs ML-based
- Real-time vs batch
- Feature engineering
- Red Flags: Only rules, no ML, no real-time
Risk Scoring
- Score calculation
- Threshold tuning
- Action determination
- Red Flags: Binary decisions, no scoring, no tuning
Bot Detection
- Behavioral analysis
- Device fingerprinting
- CAPTCHA strategies
- Red Flags: No bot detection, only CAPTCHA, no behavioral analysis

System Design Skills

Scalability
- Handle millions of users
- Real-time processing
- Database sharding
- Red Flags: Single database, no caching, no scaling plan
Performance
- Low latency requirements
- Caching strategies
- Database optimization
- Red Flags: Slow queries, no caching, N+1 queries
Reliability
- High availability
- Data consistency
- Failure handling
- Red Flags: Single point of failure, no redundancy, no monitoring

Problem-Solving Approach

Trade-off Analysis
- Security vs usability
- Accuracy vs latency
- Privacy vs detection
- Red Flags: No trade-offs, dogmatic choices
Edge Cases
- Account recovery
- False positives
- Attack scenarios
- Red Flags: Ignoring edge cases, no attack scenarios
Monitoring & Alerting
- Key metrics
- Alert thresholds
- Incident response
- Red Flags: No monitoring, no alerts, no response plan

Communication Skills

Security Explanation
- Can explain authentication flow
- Understands fraud detection
- Red Flags: Vague explanations, no understanding
Decision Justification
- Explains security choices
- Discusses alternatives
- Red Flags: No justification, no alternatives

Summary

Designing authentication and fraud detection systems for Roblox requires:

Key Components:

Authentication System: Secure registration, login, session management, 2FA
Fraud Detection System: Real-time risk scoring, bot detection, payment fraud, content fraud
Security Measures: Password hashing, rate limiting, account lockout, security alerts
ML Models: Risk scoring, anomaly detection, content classification
Real-Time Processing: Event streaming, real-time analysis, automated responses

Key Design Decisions:

Password Security: Use bcrypt/Argon2, enforce strong passwords, rate limit attempts
Session Management: JWT tokens with refresh mechanism, Redis for session storage
Fraud Detection: Hybrid approach (real-time + batch), ML models + rules engine
Scalability: Shard databases, cache sessions, use event streaming
Security: Encrypt data, rate limit, DDoS protection, compliance

Key Takeaways:

Security First: Never compromise on security, use industry best practices
User Experience: Balance security with usability (progressive challenges)
Real-Time Detection: Detect and respond to threats in real-time
Continuous Learning: Update ML models with new data and patterns
Monitoring: Monitor fraud rates, false positives, system performance

Authentication and fraud detection are critical for protecting users and the platform. The system must be secure, scalable, and provide a good user experience while effectively detecting and preventing fraud.

Introduction

Table of Contents

Problem Statement

Requirements

Functional Requirements

Non-Functional Requirements

Capacity Estimation

Traffic Estimates

Storage Estimates

Core Entities

API

Authentication APIs

Fraud Detection APIs

Data Flow

Authentication Flow

Fraud Detection Flow

Database Design

Schema Design

Database Sharding Strategy

High-Level Design

Deep Dive

Authentication System

Session Management

Multi-Factor Authentication

Account Security

Fraud Detection System

Bot Detection

Payment Fraud Detection

Content Fraud Detection

Real-Time Threat Analysis

Machine Learning Models

Security Considerations

Trade-offs and Optimizations

What Interviewers Look For

Authentication & Security Knowledge

Fraud Detection Skills

System Design Skills

Problem-Solving Approach

Communication Skills

Summary

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