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```
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
```
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