DDoS Protection - Learning Module

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Layer 7 Protection

When Volume Isn't the Problem

Your Layer 3/4 defenses are solid. You've got terabits of scrubbing capacity, anycast distribution, and hardened protocols. Then your e-commerce site goes down—and traffic looks completely normal. No bandwidth spike, no connection exhaustion, just... overwhelmed application servers.

Welcome to Layer 7 attacks, where attackers don't need massive botnets to bring down services. They need only to find the expensive endpoints—the search queries that trigger full table scans, the authentication endpoints that hash passwords, the reporting pages that generate PDFs—and request them faster than your application can respond.

L7 protection is fundamentally different from L3/L4 defense. It requires understanding your application, distinguishing bad actors from legitimate users, and making intelligent decisions about traffic that looks perfectly valid at the network level.

What You Will Learn

By the end of this page, you will understand how to design Layer 7 defenses that detect and mitigate application-layer attacks. You'll learn about HTTP flood mitigation, intelligent rate limiting, bot detection, CAPTCHA challenges, and behavioral analysis that protects applications without degrading user experience.

The Layer 7 Attack Challenge

Layer 7 attacks present unique challenges that make them particularly dangerous:

1. Low Bandwidth, High Impact:

L7 attacks can bring down services with modest traffic volumes. Consider:

A login endpoint that hashes passwords (bcrypt) takes 100ms per request
Server has 8 cores, handling ~80 concurrent requests
Attacker sends 1,000 requests/second from 100 IPs
Server overwhelmed, legitimate users can't log in
Total attack bandwidth: ~500 Kbps (trivial)

2. Legitimate Traffic Mimicry:

Unlike L3/L4 attacks with obvious signatures (malformed packets, spoofed IPs), L7 attacks use:

Valid HTTP requests
Real browser headers
Proper protocol sequences
Sometimes even valid session cookies

3. Application Awareness Required:

Effective L7 defense requires understanding your specific application:

Which endpoints are expensive?
What does normal user behavior look like?
How do legitimate bots (crawlers, monitors) behave?
What business logic can be abused?

L3/L4 vs L7 Attack Characteristics
Characteristic	L3/L4 Attacks	L7 Attacks
Traffic Volume	Very High (Gbps-Tbps)	Low to Moderate (Mbps)
Request Validity	Often malformed/spoofed	Perfectly valid
Detection	Signature/volume-based	Behavioral analysis
Mitigation	Network-level filtering	Application-aware decisions
False Positive Risk	Low	High
Impact Target	Network capacity	Application resources

The False Positive Challenge

L7 mitigation walks a tightrope between blocking attacks and blocking legitimate users. An overly aggressive block can drive away customers, damage brand reputation, and cost more than the attack itself. Every mitigation decision must weigh the cost of false positives against the cost of allowing attacks through.

Rate Limiting Strategies

Rate limiting is the foundational L7 defense mechanism. By capping how many requests a client can make within a time window, you prevent any single source from overwhelming your application.

Rate Limiting Dimensions:

Effective rate limiting considers multiple dimensions:

Rate Limiting Dimensions

•Per-IP limiting — Basic protection; easily bypassed with many IPs but catches unsophisticated attacks
•Per-user/session limiting — Protects authenticated endpoints; prevents account abuse
•Per-endpoint limiting — Different limits for different endpoints based on cost
•Global limiting — Total requests to service; emergency overflow protection
•Composite limiting — Multiple dimensions combined (IP + endpoint + time of day)

Rate Limiting Algorithms:

1. Fixed Window:

Simple: count requests in fixed time buckets (e.g., per minute)
Problem: burst attacks at window boundaries
Example: 1000 requests at 11:59:59, 1000 more at 12:00:01 = 2000 in 2 seconds

2. Sliding Window Log:

Track timestamp of every request
Count requests in trailing window
More accurate but memory-intensive

3. Sliding Window Counter:

Combine current and previous window with weights
Good balance of accuracy and efficiency
Example: 70% of current window + 30% of previous

4. Token Bucket:

Bucket fills with tokens at steady rate
Each request consumes a token
Allows bursts up to bucket size
Best for APIs with legitimate burst patterns

5. Leaky Bucket:

Requests enter queue at any rate
Processed at fixed rate
Smooths traffic but adds latency

Rate Limiting Implementation (Redis)
TypeScript
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import Redis from 'ioredis';
 
const redis = new Redis();
 
/**
 * Sliding window rate limiter using Redis sorted sets
 * Provides accurate rate limiting with efficient memory usage
 */
async function slidingWindowRateLimiter(
    identifier: string, // IP, user ID, or composite key
    limit: number,       // Max requests allowed
    windowMs: number     // Window size in milliseconds
): Promise<{ allowed: boolean; remaining: number; resetMs: number }> {
    const key = `ratelimit:${identifier}`;
    const now = Date.now();
    const windowStart = now - windowMs;
 
    // Use Redis pipeline for atomic operations
    const pipeline = redis.pipeline();
    
    // Remove entries outside the window
    pipeline.zremrangebyscore(key, 0, windowStart);
    
    // Count current entries and add new one
    pipeline.zcard(key);
    pipeline.zadd(key, now.toString(), `${now}-${Math.random()}`);
    
    // Set expiry on the key
    pipeline.expire(key, Math.ceil(windowMs / 1000) + 1);
    
    const results = await pipeline.exec();
    const currentCount = (results?.[1]?.[1] as number) || 0;
 
    if (currentCount >= limit) {
        // Over limit - remove the request we just added
        await redis.zremrangebyscore(key, now, now);
        return {
            allowed: false,
            remaining: 0,
            resetMs: windowMs - (now - windowStart)
        };
    }
 
    return {
        allowed: true,
        remaining: limit - currentCount - 1,
        resetMs: windowMs - (now - windowStart)
    };
}
 
// Usage example for different endpoint tiers
async function handleRequest(req: Request) {
    const ip = req.headers.get('x-forwarded-for') || 'unknown';
    const endpoint = new URL(req.url).pathname;
 
    // Different limits for different endpoints
    const limits: Record<string, { limit: number; window: number }> = {
        '/api/login': { limit: 5, window: 60000 },      // 5/min (expensive)
        '/api/search': { limit: 30, window: 60000 },    // 30/min (moderate)
        '/api/healthcheck': { limit: 600, window: 60000 }, // 600/min (cheap)
        'default': { limit: 100, window: 60000 }
    };
 
    const config = limits[endpoint] || limits['default'];
    const result = await slidingWindowRateLimiter(
        `${ip}:${endpoint}`,
        config.limit,
        config.window
    );
 
    if (!result.allowed) {
        return new Response('Too Many Requests', {
            status: 429,
            headers: {
                'Retry-After': Math.ceil(result.resetMs / 1000).toString(),
                'X-RateLimit-Limit': config.limit.toString(),
                'X-RateLimit-Remaining': '0'
            }
        });
    }
 
    // Continue processing...
}

Bot Detection and Mitigation

L7 attacks typically come from bots—automated scripts or compromised machines sending attack traffic. Distinguishing malicious bots from legitimate users (and legitimate bots like Googlebot) is central to L7 defense.

Bot Detection Signals:

No single signal definitively identifies a bot. Effective detection combines multiple signals:

Client-Side Signals

•JavaScript execution — Can client execute JS?
•Cookie handling — Are cookies accepted/sent?
•Browser fingerprint — Canvas, WebGL, fonts
•Mouse/keyboard events — Human movement patterns
•Timing patterns — Humanlike delays between actions
•Resource loading — CSS, images, fonts fetched?

Server-Side Signals

•Request rate — Abnormally high for an IP
•User agent — Missing, malformed, or mismatched
•Header consistency — Browser-appropriate headers
•IP reputation — Known proxy, datacenter, or bot IP
•ASN analysis — Traffic from hosting providers
•Behavioral patterns — Sequential access, no pausing

JavaScript Challenges:

The simplest bot detection: require JavaScript execution. Insert a challenge that must be solved by running JavaScript before accessing content:

Server sends page with JS challenge (compute hash, solve puzzle)
Client executes JS, computes solution
Client sends solution in subsequent request
Server validates and grants access
Simple bots fail; headless browsers proceed (but slower)

Simple JavaScript Challenge
HTML
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<!-- Challenge page served to suspicious requests -->
<!DOCTYPE html>
<html>
<head>
    <title>Security Check</title>
</head>
<body>
    <p>Verifying your browser, please wait...</p>
    <script>
        (function() {
            // Generate proof-of-work
            const challenge = document.currentScript.getAttribute('data-challenge');
            let nonce = 0;
            let solution = '';
            
            async function sha256(message) {
                const msgBuffer = new TextEncoder().encode(message);
                const hashBuffer = await crypto.subtle.digest('SHA-256', msgBuffer);
                const hashArray = Array.from(new Uint8Array(hashBuffer));
                return hashArray.map(b => b.toString(16).padStart(2, '0')).join('');
            }
            
            async function solve() {
                while (true) {
                    const hash = await sha256(challenge + nonce);
                    if (hash.startsWith('0000')) { // Difficulty: 4 leading zeros
                        solution = nonce.toString();
                        break;
                    }
                    nonce++;
                }
                
                // Submit solution
                document.cookie = `bot_check=${challenge}:${solution}; path=/; max-age=3600`;
                window.location.reload();
            }
            
            solve();
        })();
    </script>
</body>
</html>

Headless Browser Detection

Modern attack bots use headless browsers (Puppeteer, Playwright) to pass JavaScript challenges. Detection has evolved to examine browser internals—navigator properties, window dimensions, WebDriver flags, and timing characteristics that differ between headless and real browsers. This is an ongoing arms race.

CAPTCHA and Human Verification

When passive detection isn't enough, CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) provides active verification that the client is human.

CAPTCHA Evolution:

1. Text-based (Legacy):

Distorted text images
Users type what they see
Largely broken by ML image recognition
Poor accessibility

2. Image-based (reCAPTCHA v2):

"Select all images with traffic lights"
Harder for bots than text
But CAPTCHA farms solve for pennies
Frustrating user experience

3. Invisible/Behavioral (reCAPTCHA v3, hCaptcha):

No user interaction required
Analyzes behavior behind the scenes
Returns risk score (0.0-1.0)
Site decides action based on score
Best UX but requires threshold tuning

reCAPTCHA v3 Implementation
TypeScript
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// Frontend: Execute reCAPTCHA and get token
async function submitWithRecaptcha(action: string) {
    const token = await grecaptcha.execute('YOUR_SITE_KEY', { action });
    
    const response = await fetch('/api/submit', {
        method: 'POST',
        body: JSON.stringify({
            data: formData,
            recaptchaToken: token,
            expectedAction: action
        })
    });
    
    return response.json();
}
 
// Backend: Verify token and score
interface RecaptchaResponse {
    success: boolean;
    score: number;
    action: string;
    challenge_ts: string;
    hostname: string;
    'error-codes'?: string[];
}
 
async function verifyRecaptcha(
    token: string,
    expectedAction: string,
    remoteIp: string
): Promise<{ valid: boolean; score: number }> {
    const response = await fetch('https://www.google.com/recaptcha/api/siteverify', {
        method: 'POST',
        headers: { 'Content-Type': 'application/x-www-form-urlencoded' },
        body: new URLSearchParams({
            secret: process.env.RECAPTCHA_SECRET_KEY!,
            response: token,
            remoteip: remoteIp
        })
    });
 
    const result: RecaptchaResponse = await response.json();
 
    if (!result.success) {
        return { valid: false, score: 0 };
    }
 
    // Verify action matches expected
    if (result.action !== expectedAction) {
        return { valid: false, score: 0 };
    }
 
    return { valid: true, score: result.score };
}
 
// Usage with tiered response
async function handleFormSubmission(req: Request) {
    const { recaptchaToken, expectedAction } = await req.json();
    const ip = req.headers.get('x-forwarded-for') || 'unknown';
    
    const { valid, score } = await verifyRecaptcha(
        recaptchaToken,
        expectedAction,
        ip
    );
 
    if (!valid) {
        return new Response('Verification failed', { status: 403 });
    }
 
    // Tiered response based on score
    if (score >= 0.7) {
        // High confidence human - proceed normally
        return processSubmission(req);
    } else if (score >= 0.3) {
        // Medium confidence - add friction
        return requireAdditionalVerification(req);
    } else {
        // Low confidence - likely bot
        return new Response('Request blocked', { status: 403 });
    }
}

CAPTCHA Solution Comparison
Solution	User Experience	Bot Resistance	Cost	Privacy
reCAPTCHA v3	Excellent (invisible)	Good	Free	Google data collection
hCaptcha	Good	Good	Free/Paid	Privacy-focused
Cloudflare Turnstile	Excellent	Good	Free	Minimal data
Custom challenge	Variable	Moderate	Development cost	Full control

Behavioral Analysis and Anomaly Detection

The most sophisticated L7 defense examines behavior patterns rather than individual requests. Even when each request looks legitimate, attack patterns differ from normal user behavior.

Behavioral Signals:

Session-Level Analysis:

Page visit sequences (natural browsing vs. scripted patterns)
Time between requests (humans pause, bots don't)
Response to dynamic content (following links vs. hardcoded URLs)
Form fill timing (instant submission vs. typing time)
Scroll and mouse patterns (human randomness vs. bot precision)

Request-Level Analysis:

Header consistency across requests
Request ordering (assets after HTML, not before)
Error handling (humans retry, bots continue regardless)
Session cookie usage patterns

Converting Mermaid diagram...

Machine Learning for Behavior Analysis:

ML excels at detecting behavioral anomalies:

Supervised Learning:

Train on labeled human/bot session data
Classify new sessions in real-time
Requires labeled training data

Unsupervised Learning:

Cluster sessions by behavior similarity
Flag outlier clusters as suspicious
Works without labeled data

Sequence Models:

Model expected request sequences
Flag sequences that deviate significantly
Detect scripted behavior patterns

The Baseline Challenge

Behavioral analysis requires understanding 'normal' behavior—which varies by application, time of day, user segment, and device type. Mobile users behave differently than desktop users. New users explore differently than returning users. Your behavioral baseline must account for this diversity to avoid high false positive rates.

Request Validation and Filtering

Beyond rate limiting and bot detection, validating request content catches attacks that mimic legitimate traffic but contain malicious payloads or patterns.

Header Validation:

Request Header Validation
TypeScript
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interface ValidationResult {
    valid: boolean;
    reason?: string;
}
 
function validateHeaders(request: Request): ValidationResult {
    const headers = request.headers;
    
    // Check for required headers
    if (!headers.get('user-agent')) {
        return { valid: false, reason: 'Missing User-Agent' };
    }
    
    // Detect obvious bot User-Agents
    const ua = headers.get('user-agent')!;
    const botPatterns = [
        /^curl\//i,
        /^wget\//i,
        /^python-requests/i,
        /^Java\//i,
        /^Go-http-client/i
    ];
    
    for (const pattern of botPatterns) {
        if (pattern.test(ua)) {
            return { valid: false, reason: 'Bot User-Agent detected' };
        }
    }
    
    // Check for header consistency (browser should send Accept)
    if (!headers.get('accept') && ua.includes('Mozilla')) {
        return { valid: false, reason: 'Missing Accept header for browser UA' };
    }
    
    // Check Accept-Language for browsers
    if (!headers.get('accept-language') && ua.includes('Mozilla')) {
        return { valid: false, reason: 'Missing language header for browser' };
    }
    
    // Detect header order anomalies (optional, advanced)
    // Real browsers send headers in consistent orders
    
    return { valid: true };
}
 
// Payload validation
function validatePayload(body: unknown, contentType: string): ValidationResult {
    // Size limits
    const bodyString = JSON.stringify(body);
    if (bodyString.length > 1_000_000) { // 1MB reasonable limit
        return { valid: false, reason: 'Payload too large' };
    }
    
    // Validate JSON structure for API endpoints
    if (contentType.includes('application/json')) {
        // Check for deeply nested objects (DoS via parsing)
        if (getObjectDepth(body) > 20) {
            return { valid: false, reason: 'Payload too deeply nested' };
        }
        
        // Check for excessive array sizes
        if (hasLargeArrays(body, 10000)) {
            return { valid: false, reason: 'Payload arrays too large' };
        }
    }
    
    return { valid: true };
}
 
function getObjectDepth(obj: unknown, depth = 0): number {
    if (depth > 25) return depth; // Prevent stack overflow
    if (typeof obj !== 'object' || obj === null) return depth;
    
    let maxDepth = depth;
    for (const value of Object.values(obj)) {
        maxDepth = Math.max(maxDepth, getObjectDepth(value, depth + 1));
    }
    return maxDepth;
}
 
function hasLargeArrays(obj: unknown, maxSize: number): boolean {
    if (Array.isArray(obj) && obj.length > maxSize) return true;
    if (typeof obj !== 'object' || obj === null) return false;
    
    for (const value of Object.values(obj)) {
        if (hasLargeArrays(value, maxSize)) return true;
    }
    return false;
}

Request Validation Checklist

•Content-Type validation — Reject mismatched content types (JSON endpoint receiving form data)
•Payload size limits — Prevent memory exhaustion from oversized requests
•Character encoding validation — Detect encoding attacks and malformed input
•Schema validation — Validate against expected JSON/XML schemas
•Parameter pollution detection — Multiple values for single-value parameters
•Path traversal detection — Block ../.. and encoded variants in paths

Slow Attack Mitigation

Slow attacks (Slowloris, Slow POST, Slow Read) consume server resources by sending data painfully slowly, keeping connections open indefinitely. These attacks can be devastating with minimal attacker resources.

Attack Mechanics:

Slowloris (Slow Headers):

Open connection, send partial HTTP headers
Send additional header bytes every 15-30 seconds
Never complete the request
Server keeps connection open waiting

Slow POST:

Send valid headers with large Content-Length
Send body bytes very slowly (1 byte/second)
Server waits for complete body

Slow Read:

Request large response with tiny TCP window
Server must buffer response waiting to send
Fills server memory with pending responses

Slow Attack Mitigation Configuration
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# NGINX configuration to mitigate slow attacks
 
http {
    # Timeouts - most critical for slow attack defense
    
    # Time allowed between receiving consecutive bytes of headers
    # Slowloris sends bytes slowly - this catches it
    client_header_timeout 10s;
    
    # Time allowed between receiving consecutive bytes of body
    # Slow POST sends body slowly - this catches it
    client_body_timeout 10s;
    
    # Time allowed for writing response
    # Slow Read reads slowly - this helps manage it
    send_timeout 10s;
    
    # Keep-alive timeout - limit idle connection time
    keepalive_timeout 30s;
    
    # Maximum time for entire request (header + body)
    # Catches very slow attackers even if they meet per-byte requirements
    proxy_read_timeout 30s;
    
    # Buffer sizes - prevent attackers from exhausting memory
    client_header_buffer_size 1k;
    large_client_header_buffers 4 4k;
    client_max_body_size 10m;
    client_body_buffer_size 16k;
    
    # Limit connections per IP
    limit_conn_zone $binary_remote_addr zone=addr:10m;
    limit_conn addr 50;
    
    # Rate limit new connections
    limit_req_zone $binary_remote_addr zone=slowloris:10m rate=10r/s;
    
    server {
        listen 80;
        
        limit_req zone=slowloris burst=20 nodelay;
        
        location / {
            # Additional per-location timeouts if needed
            proxy_connect_timeout 5s;
            proxy_send_timeout 10s;
            proxy_read_timeout 30s;
            
            proxy_pass http://backend;
        }
    }
}

Frontend Protection

The best defense against slow attacks is not letting them reach your application servers. Place a reverse proxy, load balancer, or CDN in front that buffers complete requests before forwarding. These dedicated proxies are designed to handle many concurrent connections efficiently where application servers might not be.

Geographic and IP Intelligence

IP and geographic intelligence adds another layer of L7 defense by identifying traffic sources and applying appropriate policies.

IP Reputation:

IP reputation services maintain databases of IPs known to be:

Part of botnets
Proxy/VPN exit nodes
Tor exit nodes
Hosting provider (datacenter) IPs
Previously observed in attacks

Geographic Filtering:

If your service is regional, limiting traffic by geography reduces attack surface:

Allowlisting — Only serve expected countries/regions
Denylisting — Block countries with no legitimate users
Differential treatment — Extra verification for unexpected locations

IP Intelligence Integration
TypeScript
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import { lookup } from 'maxmind';
 
interface IPIntelligence {
    ip: string;
    country: string;
    isProxy: boolean;
    isDatacenter: boolean;
    isTor: boolean;
    isKnownBot: boolean;
    threatScore: number;
}
 
async function getIPIntelligence(ip: string): Promise<IPIntelligence> {
    // GeoIP lookup
    const geo = await lookup(ip);
    
    // IP reputation lookup (example with commercial service)
    const reputation = await fetch(`https://api.ipintel.io/check/${ip}`, {
        headers: { 'Authorization': 'Bearer ' + process.env.IP_INTEL_KEY }
    }).then(r => r.json());
    
    return {
        ip,
        country: geo?.country?.isoCode || 'UNKNOWN',
        isProxy: reputation.proxy || false,
        isDatacenter: reputation.datacenter || false,
        isTor: reputation.tor || false,
        isKnownBot: reputation.botnet || false,
        threatScore: reputation.threatScore || 0
    };
}
 
// Policy enforcement based on intelligence
async function enforceIPPolicy(req: Request): Promise<Response | null> {
    const ip = req.headers.get('x-forwarded-for')?.split(',')[0] || 'unknown';
    const intel = await getIPIntelligence(ip);
    
    // Block known bad actors
    if (intel.isKnownBot || intel.threatScore > 90) {
        return new Response('Forbidden', { status: 403 });
    }
    
    // Block Tor for sensitive endpoints
    if (intel.isTor && req.url.includes('/api/payment')) {
        return new Response('Tor not allowed for payments', { status: 403 });
    }
    
    // Geographic restrictions
    const ALLOWED_COUNTRIES = ['US', 'CA', 'GB', 'DE', 'FR'];
    if (!ALLOWED_COUNTRIES.includes(intel.country)) {
        // Don't block, but rate limit more aggressively
        // Or require additional verification
        req.headers.set('x-geo-untrusted', 'true');
    }
    
    // Datacenter IPs get extra scrutiny
    if (intel.isDatacenter) {
        // Legitimate bots (like Googlebot) come from datacenters
        // But so do most attacks
        // Apply stricter rate limits or CAPTCHA
        req.headers.set('x-datacenter-ip', 'true');
    }
    
    return null; // Continue processing
}

VPN and Proxy Considerations

Many legitimate users use VPNs for privacy. Aggressive blocking of proxy/VPN traffic will block paying customers. Consider graduated responses: extra verification for proxies rather than hard blocks. Always weigh security against user experience impact.

Summary: Layer 7 Protection

Layer 7 protection is where DDoS defense becomes intelligent. Unlike volumetric filtering, L7 defense must understand your application and distinguish bad actors from legitimate users.

Key Takeaways

•L7 attacks require modest resources — Low bandwidth can overwhelm applications by targeting expensive operations
•Rate limiting is foundational — Multiple dimensions (IP, user, endpoint) with appropriate algorithms (sliding window, token bucket)
•Bot detection combines signals — No single indicator is definitive; combine client and server-side signals
•CAPTCHA provides human verification — Invisible solutions (reCAPTCHA v3, Turnstile) balance security with user experience
•Behavioral analysis detects sophisticated attacks — Session patterns, timing, and sequences reveal automated behavior
•Request validation catches malformed traffic — Headers, payloads, and schemas should all be validated
•Slow attacks need timeout configuration — Aggressive timeouts and connection limits protect against resource exhaustion
•IP intelligence adds context — Reputation, geography, and proxy detection inform trust decisions

What's Next:

L7 protection often relies on Web Application Firewalls (WAFs) to implement these defenses at scale. The next page dives deep into WAF architecture, rule configuration, and deployment patterns that provide comprehensive application-layer protection.

Page Complete

You now understand the sophisticated world of Layer 7 DDoS protection—from rate limiting and bot detection to behavioral analysis and slow attack mitigation. Combined with L3/L4 defenses, these techniques create comprehensive protection for your applications.