HTTP flood attacks are a type of Layer 7 DDoS attack that aim to exhaust application-level resources by sending massive volumes of seemingly legitimate HTTP requests. Unlike volumetric attacks that target bandwidth, HTTP floods focus on web servers, often making detection more challenging.

Web monitoring, when implemented with the right metrics and tools, becomes an essential component for early detection and mitigation. This article explores the nature of HTTP flood attacks and outlines how network and application-layer monitoring can help you detect and defend against them in real time.

What Is an HTTP Flood Attack?

An HTTP flood occurs when an attacker or botnet sends a high volume of HTTP requests — usually GET or POST — to a web server, mimicking legitimate traffic. This type of attack exploits the stateless nature of the HTTP protocol, requiring the server to allocate resources to each request.

Key Characteristics:

• Uses standard HTTP methods (GET/POST).
• Bypasses basic firewalls by appearing legitimate.
• Doesn’t require malformed packets or protocol exploits.
• Often distributed via botnets or spoofed IPs.

Example Payloads:

GET /index.html HTTP/1.1
Host: victim.com
User-Agent: Mozilla/5.0
Accept: */*

or

POST /login HTTP/1.1
Host: victim.com
Content-Length: 85
Content-Type: application/x-www-form-urlencoded

username=admin&password=123456

These requests look normal to the server, but if repeated tens of thousands of times per second, they can overload CPU, RAM, or backend database services.

How HTTP Floods Impact Application Performance

HTTP flood attacks affect services in various ways:

Resource	Stress Effect
Web Server CPU	Overloaded with request parsing & routing
RAM	Session management and buffering strain
Database	POST floods can trigger heavy DB queries
Thread Pools	Depletion of worker threads in server pools
Network I/O	Saturation from persistent TCP connections

Because HTTP floods operate at the application layer, they consume more processing resources per request than lower-layer DDoS attacks, making them particularly dangerous.

Role of Web Monitoring in Detection

Web monitoring enables the observation of key performance indicators (KPIs) and anomalies at the HTTP application layer. With the right setup, it can detect:

• Latency spikes
• HTTP status code anomalies
• Request rate irregularities
• Behavioral deviations from known traffic baselines

Indicators of an HTTP Flood Attack (What to Monitor)

1. Spike in HTTP Request Rate

Use metrics like requests_per_second (RPS) or req/s to detect abnormal surges. If baseline is 100 RPS and traffic jumps to 10,000 RPS with no marketing campaign or SEO spike, investigate.

2. Increase in HTTP 5xx Errors

Server overload often leads to errors like 503 Service Unavailable or 504 Gateway Timeout. Monitor these codes using:

• Log aggregation (ELK stack, Loki)
• Application Performance Monitoring (APM)

3. Unusual POST Request Patterns

POST floods generate more CPU and DB load. Monitor frequency of POST requests per IP or per endpoint.

4. Geographic & ASN Anomalies

Correlate source IPs with GeoIP and ASN data. A sudden influx from unknown or non-customer regions or low-reputation ASNs (e.g., hosting providers, VPN exit nodes) could signal botnet activity.

5. Session Behavior

Look for:

• High session creation rates
• Repeated access to static endpoints
• Zero interaction with JavaScript or cookies (indicates headless clients or bots)

Sample Metrics to Track

yaml
http_requests_total
- http_response_time_ms
- active_connections
- cpu_usage_percent
- memory_usage_bytes
- http_status_count_5xx
- unique_ips_per_minute
- requests_by_user_agent
- top_requested_urls

Using Prometheus + Grafana dashboards, these metrics can be visualized and alert thresholds configured for real-time detection.

Example Attack Pattern

Timestamp	IP Address	Endpoint	Method	User-Agent	Response
10:00:01	203.0.113.4	/	GET	curl/7.58.0	200 OK
10:00:01	203.0.113.4	/login	POST	Mozilla/5.0 (FakeBot)	200 OK
10:00:01	203.0.113.5	/search?q=test	GET	Mozilla/5.0 (FakeBot)	503
…	…	…	…	…	…

How to Respond Once Detected

1. Rate Limit

Use tools like nginx limit_req_zone or CDN/WAF rate limiting to restrict access based on IP, User-Agent, or endpoint.

2. Geo-blocking and IP Reputation Filters

Block countries or IP ranges not associated with normal user traffic. Leverage services like MaxMind or IP2Location.

3. Web Application Firewall (WAF) Rules

Configure WAFs (e.g., Cloudflare, AWS WAF, F5) to:

• Challenge HEAD/GET/POST floods
• Enforce CAPTCHA or JS validation

4. Log Correlation and Alerting

Set alerts on:

• RPS thresholds
• % of 5xx responses
• Sessions/IP/minute > baseline

5. Upstream Filtering and DDoS Protection

If under attack, rely on upstream scrubbing centers to mitigate large-scale HTTP floods.

Conclusion

HTTP flood attacks pose a sophisticated threat by abusing standard HTTP functionality. Unlike brute-force DDoS, they require intelligent, real-time monitoring of application-layer traffic. Implementing web monitoring — with request rate tracking, error monitoring, and behavioral baselining — is the key to detection and mitigation.

By combining observability with WAF rules, IP intelligence, and traffic heuristics, organizations can significantly reduce the risk of downtime and maintain the performance integrity of their web applications.