WHITEPAPER

```

  Ᾰenebris: Next Gen Reverse Proxy

  Technical White Paper & Development Roadmap

  Version: 0.1.0
  Date: 2025-11-12 - Project Name: Ᾰenebris

  ---
  Abstract

  Ᾰenebris is a production grade, security first reverse proxy built in Haskell
   that aims to surpass nginx in performance, security, and developer
  experience. By leveraging Haskell's type system, STM concurrency, and the
  fast Warp web server, combined with ML based threat detection and
  intelligent routing, Ᾰenebris provides a modern alternative to traditional
  reverse proxies with native support for WebSockets, HTTP/3, streaming, and
  advanced DDoS mitigation.

  Key Innovation: While nginx requires complex configuration and external
  modules for advanced features, Ᾰenebris provides security, intelligence, and
  modern protocol support out of the box with a clean, type-safe
  architecture.

  ---
  Table of Contents

  1. #problem-statement
  2. #architecture-overview
  3. #technical-specifications
  4. #development-phases
  5. #core-components
  6. #security-model
  7. #performance-targets
  8. #deployment-strategy
  9. #long-term-roadmap
  10. #competitive-analysis

  ---
  1. Problem Statement

  Current State of Reverse Proxies

  Nginx:
  - Complex configuration syntax
  - Requires external modules for WAF, bot detection
  - WebSocket + streaming conflicts require manual tuning
  - No native ML capabilities
  - C codebase = memory safety concerns
  - Difficult to extend without C knowledge

  Traefik:
  - Resource heavy (Go runtime overhead)
  - Limited security features
  - Configuration complexity at scale

  Cloudflare:
  - External dependency
  - Privacy concerns (traffic routed through CF)
  - Cost at scale
  - No on-premise option for sensitive workloads

  What Ᾰenebris Solves

  1. Native streaming + WebSocket support - No configuration conflicts
  2. Built in ML threat detection - No external services needed
  3. Type safe configuration - Catch errors at compile time
  4. Security first design - WAF, honeypots, and DDoS protection included
  5. Production ready performance - Warp powers major Haskell web frameworks
  6. Developer friendly - Clean config, hot reload, excellent error messages
  7. Open source & self-hosted - Full control, no vendor lock in

  ---
  2. Architecture Overview

  High Level Design

  ┌─────────────────────────────────────────────────────────────┐
  │                         Ᾰenebris CORE                         │
  │                                                             │
  │  ┌──────────────┐  ┌──────────────┐  ┌──────────────┐    │
  │  │   Ingress    │  │   Analysis   │  │   Routing    │    │
  │  │   Manager    │─▶│   Engine     │─▶│   Engine     │    │
  │  └──────────────┘  └──────────────┘  └──────────────┘    │
  │         │                  │                  │            │
  │         ▼                  ▼                  ▼            │
  │  ┌──────────────────────────────────────────────────┐    │
  │  │              Connection Manager                  │    │
  │  │         (STM-based state management)             │    │
  │  └──────────────────────────────────────────────────┘    │
  │                           │                               │
  └───────────────────────────┼───────────────────────────────┘
                              │
          ┌───────────────────┼───────────────────┐
          │                   │                   │
          ▼                   ▼                   ▼
  ┌──────────────┐   ┌──────────────┐   ┌──────────────┐
  │   Backend    │   │   Backend    │   │   Honeypot   │
  │   Server 1   │   │   Server 2   │   │    Server    │
  └──────────────┘   └──────────────┘   └──────────────┘

  Component Interaction Flow

  Client Request
      │
      ▼
  ┌─────────────────────┐
  │  TLS Termination    │ (Native Haskell TLS)
  └─────────────────────┘
      │
      ▼
  ┌─────────────────────┐
  │  Protocol Handler   │ (HTTP/1.1, HTTP/2, HTTP/3, WebSocket)
  └─────────────────────┘
      │
      ▼
  ┌─────────────────────┐
  │  Rate Limiter       │ (Multi-strategy: Token Bucket, Adaptive, ML-based)
  └─────────────────────┘
      │
      ▼
  ┌─────────────────────┐
  │  WAF Scanner        │ (SQLi, XSS, Path Traversal detection)
  └─────────────────────┘
      │
      ▼
  ┌─────────────────────┐
  │  ML Bot Detector    │ (Behavioral analysis, request fingerprinting)
  └─────────────────────┘
      │
      ├─[Suspicious]──▶ Honeypot
      │
      ▼
  ┌─────────────────────┐
  │  Intelligent Router │ (Load balancing, health checks, A/B testing)
  └─────────────────────┘
      │
      ▼
  ┌─────────────────────┐
  │  Backend Proxy      │ (Zero-copy streaming, connection pooling)
  └─────────────────────┘
      │
      ▼
  Response to Client

  ---
  3. Technical Specifications

  Language & Core Libraries

  Primary Language: Haskell (GHC 9.6+)

  Core Dependencies:
  - warp (v3.3+) - HTTP server (handles 100k+ req/s)
  - wai - Web Application Interface
  - http-conduit - HTTP client for proxying
  - stm - Software Transactional Memory
  - websockets - WebSocket protocol
  - tls - TLS 1.2/1.3 support
  - http2 - HTTP/2 implementation
  - quic - HTTP/3 (QUIC) support
  - yaml / dhall - Configuration parsing
  - aeson - JSON handling
  - fast-logger - High performance logging
  - prometheus-client - Metrics export

  ML Component:
  - hmatrix - Linear algebra in Haskell
  - Models: Isolation Forest, Random Forest, LSTM for sequence analysis

  External Integrations:
  - Redis (caching, distributed rate limiting)
  - PostgreSQL/SQLite (metrics, request logs)
  - Prometheus/Grafana (observability)
  - Let's Encrypt (ACME client for SSL)

  System Requirements

  Development:
  - Linux/macOS/WSL
  - GHC 9.6+
  - Stack or Cabal
  - 4GB RAM minimum

  Production:
  - Linux (primary target)
  - 2+ CPU cores (multi-core scaling)
  - 1GB RAM minimum (scales with traffic)
  - Docker & Kubernetes support

  ---
  4. Development Phases

  Phase 0: Foundation (Week 0 - Setup)

  Duration: 2-3 daysGoal: Project scaffolding

  Tasks:
  - Set up Haskell dev environment (Stack)
  - Study Warp/WAI documentation
  - Create project structure
  - Set up Git repo + CI/CD (GitHub Actions)
  - Design config file schema (YAML)


  ---
  Phase 1: Core Proxy (Weeks 1-2)

  Duration: 2 weeksGoal: Functional reverse proxy that can replace nginx in
  dev

  Milestone 1.1: Basic HTTP Proxying (Days 1-3)
  - Parse incoming HTTP requests
  - Forward to backend server
  - Stream response back to client
  - Handle connection errors gracefully
  - Basic logging (stdout)

  Milestone 1.2: Configuration System (Days 4-5)
  - YAML config parsing
  - Define upstream backends
  - Host-based routing (virtual hosts)
  - Path-based routing
  - Config validation with type safety

  Example config:
  version: 1
  listen:
    - port: 80
    - port: 443
      tls:
        cert: /path/to/cert.pem
        key: /path/to/key.pem

  upstreams:
    - name: api-backend
      servers:
        - host: 127.0.0.1:8000
          weight: 1
        - host: 127.0.0.1:8001
          weight: 1
      health_check:
        path: /health
        interval: 10s

  routes:
    - host: api.example.com
      paths:
        - path: /
          upstream: api-backend
          rate_limit: 100/minute

  Milestone 1.3: Load Balancing (Days 6-7)
  - Round-robin algorithm
  - Least connections algorithm
  - Weighted distribution
  - Health check system (active probing)
  - Automatic backend removal on failure

  Milestone 1.4: TLS/SSL Support (Days 8-10)
  - TLS termination (Haskell tls library)
  - SNI (Server Name Indication) support
  - Cipher suite configuration
  - TLS 1.2 & 1.3 support
  - Automatic redirect HTTP → HTTPS

  Milestone 1.5: WebSocket Support (Days 11-12)
  - WebSocket handshake detection
  - Upgrade HTTP connection to WebSocket
  - Bidirectional streaming
  - Backend WebSocket proxying
  - Connection timeout handling

  Milestone 1.6: Streaming Support (Days 13-14)
  - Chunked transfer encoding
  - SSE (Server-Sent Events) support
  - No buffering for streaming responses
  - CRITICAL: Test WebSocket + streaming simultaneously (your nginx issue)
  - Verify AI model streaming works

  Phase 1 Deliverable:
  - Compiled binary (Ᾰenebris)
  - Basic config file
  - Can replace nginx for simple use cases
  - Handles your website's traffic
  - Test deployment to your projects

  ---
  Phase 2: Security & Intelligence (Weeks 3-6)

  Duration: 4 weeksGoal: Advanced security features that surpass nginx

  Milestone 2.1: Rate Limiting (Week 3)
  - Token bucket algorithm (classic)
  - Leaky bucket algorithm
  - Sliding window counters
  - Fixed window counters
  - Per-IP rate limiting
  - Per-user rate limiting (auth token tracking)
  - Per-endpoint rate limiting
  - Adaptive rate limiting (based on server load)
  - Geographic rate limiting
  - Time-of-day adjustments
  - Redis backend for distributed limiting
  - Custom rate limit responses (429 with Retry-After)

  Advanced Rate Limiting Strategies:
  data RateLimitStrategy
    = TokenBucket { capacity :: Int, refillRate :: Int }
    | LeakyBucket { capacity :: Int, leakRate :: Int }
    | SlidingWindow { windowSize :: Int, limit :: Int }
    | Adaptive { baseRate :: Int, loadFactor :: Float }
    | Behavioral { mlModel :: ModelHandle, threshold :: Float }
    | ProofOfWork { difficulty :: Int }

  Milestone 2.2: WAF (Web Application Firewall) (Week 4)
  - SQL injection detection (regex + ML)
  - XSS detection (script tag patterns, event handlers)
  - Path traversal detection (../, %2e%2e%2f)
  - Command injection detection
  - SSRF (Server-Side Request Forgery) prevention
  - CSRF token validation
  - Header injection detection
  - Multipart form bomb protection
  - JSON/XML bomb protection
  - Custom WAF rules (user-defined patterns)
  - Rule bypass detection (encoding tricks)

  Detection Engine:
  data ThreatLevel = Low | Medium | High | Critical

  data AttackSignature = AttackSignature
    { pattern :: Regex
    , threatLevel :: ThreatLevel
    , action :: Action -- Block | Log | Honeypot
    , description :: Text
    }

  -- Example signatures
  sqlInjectionSignatures :: [AttackSignature]
  xssSignatures :: [AttackSignature]
  pathTraversalSignatures :: [AttackSignature]

  Milestone 2.3: ML-Based Bot Detection (Week 5)
  - Request feature extraction (headers, timing, patterns)
  - Training data collection system
  - Isolation Forest for anomaly detection
  - Random Forest classifier (bot vs human)
  - LSTM for behavioral sequences
  - Browser fingerprinting
  - TLS fingerprinting (JA3 hash)
  - Mouse movement analysis (if JavaScript SDK added later)
  - Request entropy analysis
  - Reputation scoring system

  Features for ML Model:
  features = [
      'request_rate',           # req/sec
      'user_agent_entropy',     # Shannon entropy
      'header_count',           # number of headers
      'header_order_anomaly',   # unusual ordering
      'tls_ja3_hash',          # TLS fingerprint
      'request_method_dist',    # GET/POST ratio
      'path_entropy',          # randomness in URLs
      'referer_consistency',    # legit navigation
      'cookie_presence',        # has cookies
      'timing_variance',        # human-like delays
  ]

  Model Training Pipeline:
  - Collect legitimate traffic (labeled "human")
  - Collect bot traffic from honeypots (labeled "bot")
  - Train ensemble model (Random Forest + Isolation Forest)
  - Export to ONNX or pickle
  - Load in Haskell via FFI or HTTP API

  Milestone 2.4: DDoS Protection (Week 6)
  - SYN flood protection (SYN cookies)
  - Connection limiting (max concurrent per IP)
  - Bandwidth throttling
  - Slowloris protection (timeout slow requests)
  - HTTP flood detection (abnormal request rates)
  - Geographic blocking (block entire countries)
  - IP reputation integration (AbuseIPDB, IPQualityScore)
  - Challenge-response (CAPTCHA, proof-of-work)
  - Automatic IP blacklisting (temporary bans)
  - BGP-level mitigation (future: integrate with upstream)

  Milestone 2.5: Honeypot System (Week 6)
  - Fake backend deployment
  - Route suspicious traffic to honeypot
  - Log attacker behavior
  - Infinite response generation (tarpit)
  - Fake vulnerabilities (lure attackers)
  - Collect attack signatures for ML training
  - Integration with threat intel feeds

  Phase 2 Deliverable:
  - Security-hardened proxy
  - ML model deployment
  - Honeypot infrastructure
  - WAF rule engine
  - Production-ready security features

  ---
  Phase 3: Performance & Scale (Weeks 7-10)

  Duration: 4 weeksGoal: Optimize for production scale & performance

  Milestone 3.1: HTTP/2 Support (Week 7)
  - HTTP/2 protocol implementation
  - Server push capability
  - Stream multiplexing
  - Header compression (HPACK)
  - Priority scheduling

  Milestone 3.2: HTTP/3 (QUIC) Support (Week 8)
  - QUIC protocol integration
  - UDP-based transport
  - 0-RTT connection establishment
  - Built-in encryption
  - Loss recovery

  Milestone 3.3: Zero-Copy Optimizations (Week 9)
  - Splice syscall for direct kernel transfer
  - Sendfile for static assets
  - Memory-mapped I/O
  - Buffer pooling
  - Lazy ByteString optimization

  Milestone 3.4: Caching Layer (Week 9)
  - In-memory LRU cache
  - Redis integration for distributed caching
  - Cache invalidation strategies
  - Conditional requests (ETag, If-Modified-Since)
  - Vary header support
  - Cache key customization

  Milestone 3.5: Multi-Core Scaling (Week 10)
  - Multi-threaded request handling
  - CPU affinity tuning
  - Work-stealing scheduler
  - Non-blocking I/O everywhere
  - Benchmark on 16+ core machine

  Milestone 3.6: Connection Pooling (Week 10)
  - Backend connection reuse
  - Idle connection cleanup
  - Connection health tracking
  - Configurable pool size
  - Per-backend pools

  Performance Targets:
  - Latency: <1ms added latency (p99)
  - Throughput: 100k+ req/s on 4-core machine
  - Memory: <500MB for typical workload
  - CPU: <20% overhead vs direct connection

  Phase 3 Deliverable:
  - Production-ready performance
  - HTTP/2 & HTTP/3 support
  - Caching infrastructure
  - Benchmark results vs nginx

  ---
  Phase 4: Operations & Observability (Weeks 11-12)

  Duration: 2 weeksGoal: Production operations tooling

  Milestone 4.1: Logging & Metrics (Week 11)
  - Structured JSON logging
  - Log levels (debug, info, warn, error)
  - Access logs (Apache/nginx format compatible)
  - Error logs
  - Prometheus metrics endpoint
  - Custom metrics (request duration, backend health, etc.)
  - Grafana dashboard templates
  - OpenTelemetry integration (traces)

  Key Metrics:
  Ᾰenebris_requests_total{method, status, route}
  Ᾰenebris_request_duration_seconds{method, route}
  Ᾰenebris_backend_health{backend}
  Ᾰenebris_active_connections{backend}
  Ᾰenebris_rate_limit_hits{limiter}
  Ᾰenebris_waf_blocks{attack_type}
  Ᾰenebris_bot_detections{confidence}

  Milestone 4.2: Hot Reload (Week 11)
  - Watch config file for changes
  - Parse & validate new config
  - Swap config atomically (no dropped requests)
  - Graceful backend rotation
  - Zero-downtime deployments

  Milestone 4.3: Admin API (Week 12)
  - RESTful admin interface
  - View current config
  - View live metrics
  - Manual IP ban/unban
  - Drain backend (stop routing, wait for connections to finish)
  - Runtime config updates

  Milestone 4.4: Let's Encrypt Integration (Week 12)
  - ACME protocol client
  - Automatic cert provisioning
  - Cert renewal (30 days before expiry)
  - Multi-domain support (SAN certificates)
  - HTTP-01 challenge handling
  - DNS-01 challenge (optional, for wildcard certs)

  Phase 4 Deliverable:
  - Full observability stack
  - Hot reload capability
  - Admin API
  - Automatic SSL

  ---
  Phase 5: Deployment & Distribution (Weeks 13-14)

  Duration: 2 weeksGoal: Make it easy to install & deploy

  Milestone 5.1: Packaging (Week 13)
  - Compile static binary (musl libc)
  - Debian package (.deb)
  - RPM package (.rpm)
  - Homebrew formula (macOS)
  - AUR package (Arch Linux)
  - Nix package
  - Binary releases on GitHub

  Milestone 5.2: Docker Support (Week 13)
  - Multi-stage Dockerfile
  - Alpine-based image (<50MB)
  - Docker Compose example
  - Health check endpoint
  - Graceful shutdown (SIGTERM handling)
  - Non-root user in container

  Milestone 5.3: Kubernetes Support (Week 14)
  - Helm chart
  - Kubernetes manifests (Deployment, Service, Ingress)
  - ConfigMap for config
  - Secret management
  - Horizontal Pod Autoscaler
  - Liveness & readiness probes
  - Example ingress controller usage

  Milestone 5.4: Documentation (Week 14)
  - README with quickstart
  - Configuration reference
  - Architecture documentation
  - Performance tuning guide
  - Security best practices
  - Migration guide from nginx
  - API documentation
  - Contribution guidelines

  Milestone 5.5: Testing & CI/CD (Week 14)
  - Unit tests (HSpec)
  - Integration tests
  - Performance benchmarks (criterion)
  - Load testing (hey, wrk)
  - GitHub Actions CI
  - Automated releases
  - Docker image builds

  Phase 5 Deliverable:
  - Installable packages for major distros
  - Docker & Kubernetes support
  - Complete documentation
  - Automated testing & releases

  ---
  5. Core Components

  5.1 Ingress Manager

  Responsibility: Accept incoming connections, TLS termination, protocol
  detection

  Implementation:
  data IngressConfig = IngressConfig
    { listenPorts :: [Port]
    , tlsConfig :: Maybe TLSConfig
    , maxConnections :: Int
    , connectionTimeout :: NominalDiffTime
    }

  ingressManager :: IngressConfig -> IO ()
  ingressManager config = do
    runSettings (warpSettings config) $ \req respond -> do
      -- Protocol detection
      protocol <- detectProtocol req
      case protocol of
        HTTP -> handleHTTP req respond
        WebSocket -> handleWebSocket req respond
        HTTP2 -> handleHTTP2 req respond
        HTTP3 -> handleHTTP3 req respond

  Key Features:
  - Multi-port listening (80, 443, custom)
  - SNI support for multi-domain TLS
  - Connection limiting
  - Protocol detection (HTTP/1.1, HTTP/2, HTTP/3, WebSocket)

  ---
  5.2 Analysis Engine

  Responsibility: Security scanning, bot detection, WAF

  Implementation:
  data AnalysisResult
    = Clean
    | Suspicious ThreatLevel [ThreatIndicator]
    | Malicious AttackType

  data ThreatIndicator
    = SQLInjection Pattern
    | XSSAttempt Pattern
    | BotBehavior Float -- confidence score
    | RateLimitExceeded
    | IPReputationLow

  analyzeRequest :: Request -> IO AnalysisResult
  analyzeRequest req = do
    wafResult <- runWAFChecks req
    botScore <- mlBotDetector req
    rateLimit <- checkRateLimit req
    reputation <- checkIPReputation (remoteHost req)

    return $ aggregateResults [wafResult, botScore, rateLimit, reputation]

  Security Layers:
  1. WAF Scanner - Regex + pattern matching
  2. ML Bot Detector - Behavioral analysis
  3. Rate Limiter - Multiple strategies
  4. IP Reputation - External threat feeds

  ---
  5.3 Routing Engine

  Responsibility: Intelligent request routing, load balancing, A/B testing

  Implementation:
  data Route = Route
    { matcher :: RequestMatcher
    , upstream :: Upstream
    , middleware :: [Middleware]
    }

  data RequestMatcher
    = HostMatch Hostname
    | PathMatch PathPattern
    | HeaderMatch HeaderName HeaderValue
    | Composite [RequestMatcher]

  data Upstream = Upstream
    { backends :: [Backend]
    , balancer :: LoadBalancer
    , healthCheck :: HealthCheckConfig
    }

  data LoadBalancer
    = RoundRobin
    | LeastConnections
    | Weighted [(Backend, Int)]
    | IPHash
    | LatencyBased

  Routing Strategies:
  - Host-based (virtual hosts)
  - Path-based (URL routing)
  - Header-based (A/B testing, canary)
  - Geographic routing
  - Latency-based routing

  ---
  5.4 Connection Manager

  Responsibility: Backend connection pooling, health tracking

  Implementation:
  data ConnectionPool = ConnectionPool
    { available :: TVar [Connection]
    , inUse :: TVar (Set Connection)
    , maxSize :: Int
    , backend :: Backend
    }

  acquireConnection :: ConnectionPool -> IO Connection
  acquireConnection pool = atomically $ do
    avail <- readTVar (available pool)
    case avail of
      (conn:rest) -> do
        writeTVar (available pool) rest
        modifyTVar' (inUse pool) (Set.insert conn)
        return conn
      [] -> retry -- STM will block until connection available

  releaseConnection :: ConnectionPool -> Connection -> IO ()
  releaseConnection pool conn = atomically $ do
    modifyTVar' (inUse pool) (Set.delete conn)
    modifyTVar' (available pool) (conn:)

  Features:
  - Per-backend connection pools
  - Automatic connection recycling
  - Health-based connection invalidation
  - Configurable pool size

  ---
  5.5 ML Bot Detection System

  Architecture:

  ┌─────────────────────────────────────────────────────┐
  │                   Ᾰenebris Proxy                      │
  │                                                     │
  │  ┌──────────────────────────────────────────────┐ │
  │  │         Feature Extractor (Haskell)          │ │
  │  │  - Parse request headers                     │ │
  │  │  - Calculate entropy, timing, patterns       │ │
  │  │  - Extract TLS fingerprint                   │ │
  │  └──────────────────────────────────────────────┘ │
  │                      │                              │
  │                      ▼                              │
  │  ┌──────────────────────────────────────────────┐ │
  │  │      ML Model Inference (Python Service)     │ │
  │  │  - Load trained model (pickle/ONNX)          │ │
  │  │  - Predict: bot probability                  │ │
  │  │  - Return confidence score                   │ │
  │  └──────────────────────────────────────────────┘ │
  │                      │                              │
  │                      ▼                              │
  │  ┌──────────────────────────────────────────────┐ │
  │  │         Decision Engine (Haskell)            │ │
  │  │  - If score > 0.8 → Honeypot                │ │
  │  │  - If score > 0.5 → Rate limit               │ │
  │  │  - If score < 0.5 → Allow                    │ │
  │  └──────────────────────────────────────────────┘ │
  └─────────────────────────────────────────────────────┘

  Training Pipeline:

  1. Data Collection:
    - Legitimate traffic: Your production logs
    - Bot traffic: Honeypot captures, public datasets
  2. Feature Engineering:
  def extract_features(request):
      return {
          'request_rate': calculate_rate(request.ip),
          'ua_entropy': shannon_entropy(request.user_agent),
          'header_count': len(request.headers),
          'tls_fingerprint': ja3_hash(request.tls_info),
          'timing_variance': np.std(request.timings),
          # ... 20+ features
      }
  3. Model Training:
  from sklearn.ensemble import RandomForestClassifier, IsolationForest

  # Supervised: Random Forest
  rf = RandomForestClassifier(n_estimators=100)
  rf.fit(X_train, y_train)

  # Unsupervised: Isolation Forest (anomaly detection)
  iso = IsolationForest(contamination=0.1)
  iso.fit(X_legitimate)

  # Ensemble
  def predict(features):
      rf_score = rf.predict_proba(features)[1]
      iso_score = iso.decision_function(features)
      return 0.7 * rf_score + 0.3 * normalize(iso_score)
  4. Deployment:
    - Export model to ONNX
    - Load in Python microservice (FastAPI)
    - Haskell calls via HTTP (POST /predict)
    - Cache predictions (1 min TTL per IP)

  Continuous Learning:
  - Feedback loop: Honeypot captures → retrain model
  - Weekly model updates
  - A/B test new models before deployment

  ---
  6. Security Model

  6.1 Threat Model

  Attackers We Defend Against:
  1. Script kiddies - Automated scanners, known exploits
  2. Bot operators - Credential stuffing, scraping, spam
  3. DDoS attackers - Volumetric attacks, application-layer floods
  4. Sophisticated attackers - 0-day exploits, APTs (defense-in-depth)

  Assets We Protect:
  - Backend services (API, web apps)
  - User data (prevent exfiltration)
  - System availability (uptime)
  - Infrastructure costs (prevent resource exhaustion)

  6.2 Security Principles

  1. Defense in Depth: Multiple layers (WAF → ML → Rate Limiting)
  2. Fail Secure: Errors block traffic, not allow it
  3. Least Privilege: Proxy runs as non-root user
  4. Audit Everything: All security events logged
  5. Type Safety: Haskell prevents memory corruption, buffer overflows

  6.3 WAF Rule Engine

  Rule Format:
  waf_rules:
    - name: sql-injection-basic
      pattern: "(?i)(union|select|insert|update|delete|drop|create|alter)\\s"
      threat_level: high
      action: block

    - name: xss-script-tag
      pattern: "<script[^>]*>.*?</script>"
      threat_level: high
      action: block

    - name: path-traversal
      pattern: "\\.\\./|%2e%2e%2f"
      threat_level: medium
      action: log_and_block

  Custom Rules:
  Users can add their own regex patterns via config.

  6.4 IP Reputation System

  Data Sources:
  - AbuseIPDB API
  - IPQualityScore API
  - Spamhaus DROP list
  - Local blacklist/whitelist

  Scoring System:
  data ReputationScore = ReputationScore
    { score :: Float -- 0.0 (bad) to 1.0 (good)
    , sources :: [ReputationSource]
    , lastUpdated :: UTCTime
    }

  calculateReputation :: IP -> IO ReputationScore
  calculateReputation ip = do
    abuseScore <- queryAbuseIPDB ip
    qualityScore <- queryIPQuality ip
    spamhausListed <- checkSpamhaus ip
    localScore <- checkLocalLists ip

    return $ aggregateScores [abuseScore, qualityScore, spamhausListed,
  localScore]

  Actions Based on Score:
  - Score < 0.3: Block immediately
  - Score 0.3-0.6: Rate limit aggressively
  - Score 0.6-0.8: Normal rate limits
  - Score > 0.8: Trusted, higher limits

  ---
  7. Performance Targets

  7.1 Benchmarks

  Target Performance (4-core machine, 16GB RAM):

  | Metric        | Target            | Stretch Goal |
  |---------------|-------------------|--------------|
  | Requests/sec  | 100,000           | 200,000      |
  | Latency (p50) | <0.5ms            | <0.3ms       |
  | Latency (p99) | <2ms              | <1ms         |
  | Memory usage  | <500MB            | <300MB       |
  | CPU overhead  | <20%              | <10%         |
  | Connections   | 10,000 concurrent | 50,000       |

  Comparison to Nginx:
  - Match or exceed nginx performance on similar hardware
  - Lower latency for WebSocket/streaming workloads
  - Comparable or better throughput for HTTP/2

  7.2 Optimization Techniques

  Haskell-Specific:
  - Strictness annotations to avoid space leaks
  - Unboxed types for performance-critical paths
  - INLINE pragmas for hot functions
  - Compiled with -O2 optimization
  - Profile-guided optimization (PGO)

  System-Level:
  - Zero-copy via splice() syscall
  - SO_REUSEPORT for multi-core scaling
  - TCP_NODELAY for low latency
  - Large buffer sizes for throughput
  - Kernel bypass (io_uring) for extreme performance (future)

  Application-Level:
  - Connection pooling (reuse backend connections)
  - HTTP keep-alive
  - Request pipelining
  - Lazy evaluation for streaming
  - STM for lock-free concurrency

  7.3 Benchmark Suite

  Tools:
  - wrk - HTTP benchmarking
  - h2load - HTTP/2 benchmarking
  - hey - Load testing
  - criterion - Haskell microbenchmarks

  Test Scenarios:
  1. Static file serving (1KB, 10KB, 100KB)
  2. Simple proxy (echo server backend)
  3. WebSocket throughput
  4. Streaming response (chunked transfer)
  5. TLS handshake performance
  6. HTTP/2 multiplexing
  7. Rate limiting overhead
  8. WAF scanning overhead

  Continuous Benchmarking:
  - Run benchmarks on every commit (GitHub Actions)
  - Track performance regression
  - Publish results publicly

  ---
  8. Deployment Strategy

  8.1 Installation Methods

  Binary Installation:
  # Linux (curl)
  curl -sSL https://get.Ᾰenebris.sh | sh

  # Homebrew (macOS/Linux)
  brew install Ᾰenebris

  # Debian/Ubuntu
  sudo apt install Ᾰenebris

  # Arch Linux
  yay -S Ᾰenebris

  From Source:
  git clone https://github.com/username/Ᾰenebris
  cd Ᾰenebris
  stack build
  stack install

  Docker:
  docker pull Ᾰenebris/Ᾰenebris:latest
  docker run -p 80:80 -p 443:443 -v ./config.yaml:/etc/Ᾰenebris/config.yaml
  Ᾰenebris/Ᾰenebris

  Kubernetes:
  helm repo add Ᾰenebris https://charts.Ᾰenebris.sh
  helm install my-proxy Ᾰenebris/Ᾰenebris

  8.2 Configuration Example

  Minimal Config:
  version: 1

  listen:
    - port: 80
    - port: 443
      tls:
        auto: true  # Let's Encrypt

  upstreams:
    - name: my-app
      servers:
        - host: localhost:8000

  routes:
    - host: example.com
      upstream: my-app

  Advanced Config:
  version: 1

  global:
    worker_threads: 4
    max_connections: 10000
    log_level: info

  listen:
    - port: 80
    - port: 443
      tls:
        auto: true
        email: admin@example.com

  upstreams:
    - name: api-backend
      servers:
        - host: 10.0.1.10:8000
          weight: 2
        - host: 10.0.1.11:8000
          weight: 1
      balancer: weighted
      health_check:
        path: /health
        interval: 10s
        timeout: 2s
      connection_pool:
        size: 100
        idle_timeout: 60s

    - name: honeypot
      servers:
        - host: localhost:9999

  routes:
    - host: api.example.com
      paths:
        - path: /api/v1
          upstream: api-backend
          rate_limit:
            strategy: adaptive
            base_rate: 100/minute
          waf:
            enabled: true
            rules: [sql-injection, xss, path-traversal]
          cache:
            enabled: true
            ttl: 60s

  security:
    waf:
      enabled: true
      custom_rules: /etc/Ᾰenebris/waf-rules.yaml

    bot_detection:
      enabled: true
      ml_model: /var/lib/Ᾰenebris/models/bot-detector.onnx
      threshold: 0.7
      action: honeypot

    ddos:
      max_connections_per_ip: 100
      syn_flood_protection: true
      rate_limit:
        global: 10000/second
        per_ip: 100/second

    ip_reputation:
      providers:
        - abuseipdb:
            api_key: ${ABUSEIPDB_API_KEY}
        - ipqualityscore:
            api_key: ${IPQS_API_KEY}
      cache_ttl: 3600

  observability:
    access_log: /var/log/Ᾰenebris/access.log
    error_log: /var/log/Ᾰenebris/error.log
    metrics:
      enabled: true
      port: 9090
      path: /metrics

  8.3 Migration from Nginx

  Migration Tool:
  Ᾰenebris migrate --from nginx --config /etc/nginx/nginx.conf --out
  Ᾰenebris-config.yaml

  Converts nginx config to Ᾰenebris config (best-effort).

  Migration Guide:
  1. Install Ᾰenebris alongside nginx
  2. Convert config with migration tool
  3. Test Ᾰenebris with subset of traffic
  4. Gradually shift traffic (DNS, load balancer)
  5. Monitor metrics, compare performance
  6. Full cutover once confident

  ---
  9. Long-Term Roadmap

  Year 1: Core Features & Adoption

  Q1 2025 (Months 1-3):
  - Phase 1: Core proxy functionality
  - Phase 2: Security features (WAF, ML, rate limiting)
  - First production deployment (your website)

  Q2 2025 (Months 4-6):
  - Phase 3: Performance optimization (HTTP/2, HTTP/3, caching)
  - Phase 4: Observability (metrics, logging, hot reload)
  - Phase 5: Packaging & distribution
  - Public beta release
  - First 100 GitHub stars

  Q3 2025 (Months 7-9):
  - Performance tuning based on real-world usage
  - Bug fixes & stability improvements
  - Community feedback integration
  - First external production deployments
  - 1,000 GitHub stars
  - Featured on Hacker News

  Q4 2025 (Months 10-12):
  - v1.0 stable release
  - Security audit (external firm)
  - Performance benchmarks published
  - Case studies from early adopters
  - 5,000 GitHub stars
  - First paid support contracts

  ---
  Year 2: Enterprise Features

  Q1 2026:
  - Multi-tenancy support
  - Advanced analytics dashboard (web UI)
  - Rate limiting marketplace (community rules)
  - Plugin system (extend with Haskell modules)

  Q2 2026:
  - Clustering & high availability
  - Distributed caching (beyond Redis)
  - Geographic load balancing
  - Edge computing support

  Q3 2026:
  - gRPC proxying
  - Service mesh integration (Istio, Linkerd)
  - Advanced observability (distributed tracing)
  - Chaos engineering tools

  Q4 2026:
  - Enterprise SLA & support
  - Cloud marketplace listings (AWS, GCP, Azure)
  - Certification program
  - Annual conference (ᾸenebrisCon?)

  ---
  Year 3+: Ecosystem & Innovation

  Long-Term Vision:
  - De facto standard for security-first proxying
  - Larger community than Caddy
  - Competitive with nginx in market share
  - Research papers on ML-based threat detection
  - University curriculum adoption
  - Funding (VC or grants) for full-time development
  - Commercial entity (dual-license: OSS + enterprise)

  Moonshot Features:
  - Quantum-resistant TLS (post-quantum crypto)
  - Zero-knowledge proof authentication
  - Fully homomorphic encryption proxying
  - AI-powered auto-tuning (self-optimizing)
  - Blockchain-based threat intel sharing
  - Formal verification of security properties

  ---
  10. Competitive Analysis

  10.1 Ᾰenebris vs. Nginx

  | Feature               | Nginx                      | Ᾰenebris
           |
  |-----------------------|----------------------------|---------------------
  ---------|
  | Language              | C                          | Haskell
           |
  | Type Safety           | Manual memory management | Compile-time
  guarantees    |
  | Config Syntax         | Custom DSL (complex)       | YAML (simple,
  familiar)      |
  | WebSocket + Streaming | ⚠ Conflicting settings    | Works out of the
  box       |
  | WAF                   | Requires ModSecurity     | Built-in
             |
  | ML Bot Detection      | External service needed  | Built-in
             |
  | Rate Limiting         | ⚠ Basic (needs modules)   | Advanced (ML,
  adaptive)    |
  | HTTP/3                | ⚠ Experimental            | Production-ready
  (planned) |
  | Hot Reload            | ⚠ Graceful restart        | Zero-downtime
            |
  | Performance           | ⚡ 100k+ req/s              | ⚡ 100k+ req/s
  (target)       |
  | Memory Safety         | C vulnerabilities        | Haskell safety
             |
  | Extensibility         | C modules only           | Haskell plugins
             |

  When to use Nginx:
  - Extreme performance requirements (>500k req/s)
  - Existing nginx expertise
  - Specific modules not in Ᾰenebris yet

  When to use Ᾰenebris:
  - Security-first requirements
  - Modern protocols (HTTP/3, WebSocket)
  - Clean configuration
  - ML-based threat detection
  - Self-hosting with strong privacy needs

  ---
  10.2 Ᾰenebris vs. Traefik

  | Feature           | Traefik               | Ᾰenebris                     |
  |-------------------|-----------------------|----------------------------|
  | Language          | Go                    | Haskell                    |
  | Config            | Dynamic (labels, API) | Static (YAML) + hot reload |
  | Kubernetes Native | Ingress controller  | Helm chart
  |
  | Let's Encrypt     | Built-in            | Built-in
  |
  | WAF               | Plugin needed       | Built-in
  |
  | ML Features       | None                | Bot detection
  |
  | Performance       | ⚠ Go overhead        | Haskell optimized        |
  | Memory Usage      | ⚠ High (Go runtime)  | Lower                    |

  When to use Traefik:
  - Heavy Kubernetes usage
  - Need dynamic config via API
  - Go ecosystem familiarity

  When to use Ᾰenebris:
  - Better performance
  - Advanced security (WAF, ML)
  - Lower resource usage

  ---
  10.3 Ᾰenebris vs. Caddy

  | Feature           | Caddy              | Ᾰenebris                |
  |-------------------|--------------------|-----------------------|
  | Language          | Go                 | Haskell               |
  | Ease of Use       | Extremely simple | Simple but powerful |
  | Auto HTTPS        | Best-in-class    | Built-in            |
  | Security Features | ⚠ Basic           | Advanced (WAF, ML)  |
  | Performance       | ⚠ Good            | Better              |
  | Extensibility     | Go plugins       | Haskell plugins     |

  When to use Caddy:
  - Simplicity is priority #1
  - Quick prototyping

  When to use Ᾰenebris:
  - Production security requirements
  - Performance-critical applications
  - Advanced threat detection

  ---
  10.4 Ᾰenebris vs. Cloudflare

  | Feature         | Cloudflare              | Ᾰenebris          |
  |-----------------|-------------------------|-----------------|
  | Deployment      | Cloud (SaaS)            | Self-hosted     |
  | Privacy         | Traffic via CF        | Full control  |
  | DDoS Protection | Best (global network) | ⚠ Good (local) |
  | WAF             | Extensive rules       | Built-in      |
  | Bot Detection   | ML-based              | ML-based      |
  | Cost            | $$$ at scale            | Free (OSS)    |
  | On-Premise      | Not available         | Yes           |

  When to use Cloudflare:
  - Need global CDN
  - Massive DDoS attacks (Tbps)
  - No ops team

  When to use Ᾰenebris:
  - Privacy/compliance requirements
  - Self-hosting preference
  - Cost optimization
  - Custom logic needed

  ---
  11. Success Metrics

  Technical Metrics

  - 100k+ req/s sustained throughput
  - <1ms p99 latency
  - 99.99% uptime in production
  - Zero CVEs in first year
  - Pass security audit

  Adoption Metrics

  - 1,000 GitHub stars (Month 6)
  - 5,000 GitHub stars (Month 12)
  - 10,000 GitHub stars (Month 18)
  - 100 production deployments (Month 12)
  - 10 enterprise users (Month 18)

  Community Metrics

  - 50+ contributors (Month 12)
  - 100+ issues/PRs (Month 12)
  - Active Discord/Slack community
  - Monthly blog posts
  - Conference talks (3+ in Year 1)

  Financial Metrics (Optional)

  - Paid support contracts (5+ by Month 18)
  - Sponsorships (GitHub Sponsors, Patreon)
  - Grant funding (Mozilla MOSS, NLNet)
  - Break-even on hosting/infra costs

  ---
  12. Risk Analysis & Mitigation

  Technical Risks

  Risk: Performance doesn't match nginx
  - Mitigation: Benchmark early and often, optimize hot paths, use profiling
  tools
  - Fallback: Focus on "good enough" performance + superior features

  Risk: Haskell learning curve too steep
  - Mitigation: Detailed documentation, example code, community support
  - Fallback: Add maintainers with Haskell expertise

  Risk: ML models have high false positive rate
  - Mitigation: Extensive training data, human-in-the-loop validation,
  adjustable thresholds
  - Fallback: Make ML optional, fall back to heuristics

  Risk: Memory leaks in long-running process
  - Mitigation: Strict evaluation, profiling, extensive testing
  - Fallback: Automatic restart on memory threshold

  Adoption Risks

  Risk: "Yet another reverse proxy" fatigue
  - Mitigation: Clear differentiation (security, ML, Haskell), compelling
  demos
  - Fallback: Target niche (security-conscious devs) first

  Risk: Lack of community contributions
  - Mitigation: Good-first-issue labels, responsive maintainers, contributor
  guide
  - Fallback: Solo development sustainable with clear roadmap

  Risk: Enterprise users need support
  - Mitigation: Paid support offering, SLA guarantees
  - Fallback: Community support + consulting services

  Operational Risks

  Risk: Security vulnerability discovered
  - Mitigation: Security audits, bug bounty, rapid patch releases
  - Fallback: Transparent disclosure, immediate fixes

  Risk: Funding for full-time development
  - Mitigation: Sponsorships, grants, paid support
  - Fallback: Nights-and-weekends development sustainable

  ---
  13. Open Questions & Research

  Questions to Answer During Development

  1. ML Model Deployment:
    - Python microservice vs. Haskell FFI vs. ONNX runtime?
    - How to update models without downtime?
  2. Configuration Language:
    - YAML vs. Dhall vs. custom DSL?
    - How much validation at parse time vs. runtime?
  3. Plugin Architecture:
    - Dynamic loading or compile-time plugins?
    - How to ensure type safety with plugins?
  4. Observability:
    - OpenTelemetry vs. custom tracing?
    - Push vs. pull metrics?
  5. High Availability:
    - Active-active vs. active-passive clustering?
    - Shared state via Redis or consensus (Raft)?

  Areas for Research

  - Formal Verification: Prove security properties using Liquid Haskell or
  Coq
  - Zero-Copy Proxy: eBPF or io_uring for kernel-bypass networking
  - Post-Quantum TLS: Integrate NIST PQC standards as they finalize
  - Edge Computing: Deploy Ᾰenebris on edge nodes (Cloudflare Workers model)
  - AI Security: Use LLMs to generate WAF rules or analyze attack patterns

  ---
  14. Project Name & Branding

  Name: Ᾰenebris
  - Velocity-Optimized Routing & Threat EXtermination
  - Implies: Fast, powerful, pulls everything in (like a Ᾰenebris)
  - Short, memorable, domain available

  Tagline Options:
  1. "The secure, intelligent reverse proxy"
  2. "Where speed meets security"
  3. "Next-gen proxy, built in Haskell"
  4. "nginx, but with a brain"

  Logo Ideas:
  - Stylized Ᾰenebris/spiral (purple/blue gradient)
  - Shield + lightning bolt (security + speed)
  - Geometric pattern (type safety, mathematical)

  Domain: Ᾰenebris.sh or Ᾰenebrisproxy.io

  ---
  15. Call to Action

  Next Steps (You)

  1. Set up dev environment (Day 1)
    - Install Stack
    - Clone starter template
    - Run "Hello World" Warp server
  2. Learn Haskell basics (Days 2-3)
    - Read LYAH chapters 1-8
    - Complete 10 small exercises
    - Understand monads (IO, Maybe, Either)
  3. Build Phase 1 Milestone 1 (Days 4-7)
    - Basic HTTP proxy
    - Forward request to localhost:8000
    - Log request/response
  4. Weekly check-ins
    - Review progress
    - Adjust roadmap
    - Pair program on hard parts

  Next Steps (AI Agents)

  - Agent 1: Documentation & examples
  - Agent 2: Testing & benchmarking
  - Agent 3: ML model training
  - Agent 4: Packaging & distribution

  All agents can read this white paper to stay aligned.

  ---
  16. Conclusion

  Ᾰenebris is an ambitious project to build a production-grade reverse proxy
  that rivals nginx in performance while surpassing it in security,
  intelligence, and developer experience. By leveraging Haskell's type
  safety, STM concurrency, and the Warp web server, combined with ML-based
  threat detection and modern protocol support, Ᾰenebris aims to become the
  go-to choice for security-conscious developers and enterprises.

  The journey:
  - Weeks 1-2: Basic proxy (replace nginx in dev)
  - Weeks 3-6: Security features (WAF, ML, DDoS)
  - Weeks 7-10: Performance (HTTP/2, HTTP/3, caching)
  - Weeks 11-14: Operations (metrics, packaging, docs)
  - Month 4+: Production hardening, community growth

  The vision:
  - Year 1: Stable v1.0, first 1000 users
  - Year 2: Enterprise features, major adoption
  - Year 3+: Industry standard, self-sustaining ecosystem

  Let's build the future of reverse proxies. Let's build Ᾰenebris. 🚀

  ---
  Document Version: 0.1.0Last Updated: 2025-11-12Author: Carter Perez (+
  Claude AI)License: MIT (code) / CC BY-SA 4.0 (this document)Status: Living
  document (will evolve as project progresses)

  ---
  Appendix A: Reference Architecture Diagram

                                      ┌─────────────────────────────────────┐
                                      │         Internet / Clients          │
                                      └─────────────────────────────────────┘
                                                      │
                                                      │ HTTP/HTTPS/HTTP3/WS
                                                      ▼

  ┌───────────────────────────────────────────────────────────┐
                      │                      Ᾰenebris PROXY
       │
                      │
       │
                      │
  ┌─────────────────────────────────────────────────────┐ │
                      │  │              Ingress Manager
    │ │
                      │  │  • TLS Termination (Let's Encrypt)
    │ │
                      │  │  • Protocol Detection (HTTP/1.1, HTTP/2, HTTP/3)
    │ │
                      │  │  • Connection Limiting
    │ │
                      │
  └─────────────────────────────────────────────────────┘ │
                      │                           │
       │
                      │                           ▼
       │
                      │
  ┌─────────────────────────────────────────────────────┐ │
                      │  │              Security Layer
    │ │
                      │  │  ┌────────────┐ ┌────────────┐ ┌─────────────┐
  │ │
                      │  │  │    WAF     │ │ ML Bot Det │ │ Rate Limiter│
  │ │
                      │  │  └────────────┘ └────────────┘ └─────────────┘
  │ │
                      │  │         │              │              │
    │ │
                      │  │         └──────────────┴──────────────┘
    │ │
                      │  │                       │
     │ │
                      │  │              Clean ───┴─── Malicious
    │ │
                      │  │                 │            │
     │ │
                      │
  └─────────────────┼────────────┼───────────────────── │ │
                      │                    │            │
      │
                      │                    ▼            ▼
      │
                      │  ┌─────────────────────────┐ ┌──────────────────────┐
    │
                      │  │   Routing Engine        │ │   Honeypot           │
    │
                      │  │  • Load Balancing       │ │  • Tarpit            │
    │
                      │  │  • Health Checks        │ │  • Data Collection   │
    │
                      │  │  • A/B Testing          │ │                      │
    │
                      │  └─────────────────────────┘ └──────────────────────┘
    │
                      │                │                         │
      │
                      │                ▼                         │
      │
                      │  ┌─────────────────────────────────────┐│
     │
                      │  │    Connection Pool Manager          ││
     │
                      │  │  • Per-backend pools                ││
     │
                      │  │  • Connection reuse                 ││
     │
                      │  └─────────────────────────────────────┘│
     │
                      │                │                         │
      │

  └────────────────┼─────────────────────────┼───────────────┘
                                       │                         │
                                       ▼                         ▼
              ┌──────────────────────────────────────┐  ┌─────────────────┐
              │         Backend Services             │  │  Fake Backend   │
              │                                      │  │  (Honeypot)     │
              │  ┌─────────┐  ┌─────────┐           │  └─────────────────┘
              │  │ API 1   │  │ API 2   │  ...      │
              │  └─────────┘  └─────────┘           │
              └──────────────────────────────────────┘

                           Observability Stack
              ┌──────────────────────────────────────────────┐
              │  Prometheus  │  Grafana  │  Logs  │  Traces │
              └──────────────────────────────────────────────┘

  ---
  Appendix B: Technology Stack Summary

  | Layer         | Technology                     | Purpose
                  |
  |---------------|--------------------------------|-------------------------
  ----------------|
  | Core Language | Haskell (GHC 9.6+)             | Type-safe, concurrent,
  high-performance |
  | Web Server    | Warp                           | HTTP server (fastest in
  Haskell)        |
  | Concurrency   | STM, Async                     | Lock-free state,
  parallel tasks         |
  | TLS           | tls library                    | TLS 1.2/1.3 termination
                  |
  | HTTP/2        | http2 library                  | Protocol support
                  |
  | HTTP/3        | quic library                   | QUIC implementation
                  |
  | WebSocket     | websockets library             | WebSocket protocol
                  |
  | Config        | YAML / Dhall                   | Human-readable
  configuration            |
  | Logging       | fast-logger                    | High-performance
  structured logs        |
  | Metrics       | prometheus-client              | Prometheus-compatible
  metrics           |
  | ML            | Python (scikit-learn, PyTorch) | Bot detection models
                  |
  | ML Inference  | ONNX Runtime or HTTP API       | Model serving
                  |
  | Caching       | Redis                          | Distributed cache & rate
   limiting       |
  | Database      | PostgreSQL / SQLite            | Metrics, request logs
                  |
  | Packaging     | Stack / Cabal                  | Build system
                  |
  | Containers    | Docker, Kubernetes             | Deployment
                  |
  | CI/CD         | GitHub Actions                 | Automated testing &
  releases            |

  ---
  Appendix C: Glossary

  Terms:

  - Reverse Proxy: Server that forwards client requests to backend servers
  - Load Balancer: Distributes traffic across multiple backend servers
  - WAF: Web Application Firewall - filters malicious HTTP traffic
  - DDoS: Distributed Denial of Service - attack that overwhelms server
  - Rate Limiting: Restricts number of requests per time period
  - Honeypot: Fake server to lure and study attackers
  - STM: Software Transactional Memory - lock-free concurrency primitive
  - Zero-Copy: Technique to avoid copying data between buffers
  - TLS Termination: Decrypting HTTPS at proxy, forwarding HTTP to backend
  - Connection Pooling: Reusing TCP connections to backend servers
  - HTTP/2: Binary HTTP protocol with multiplexing
  - HTTP/3: HTTP over QUIC (UDP-based, faster than TCP)
  - WebSocket: Protocol for bidirectional communication over single TCP
  connection
  - SNI: Server Name Indication - TLS extension for virtual hosting
  - ACME: Automated Certificate Management Environment (Let's Encrypt
  protocol)

  ---
  END OF WHITE PAPER
```