HTTP Flood attacks are a common type of application-layer DDoS attack that targets web servers by overwhelming them with a massive number of HTTP requests.
Attackers send a high volume of seemingly legitimate HTTP GET or POST requests to the target web server. These requests often include valid headers and payloads, making it challenging for traditional security systems to differentiate between malicious and legitimate traffic. The objective is to overload the server’s processing capacity, rendering it unable to serve legitimate users.
What HTTP Floods Look Like in Web Logs
1. High Volume of Repeated GET Requests
Attackers often target specific endpoints, sending repeated HTTP GET requests to overload the server.
Log Example (Apache/NGINX Logs):
[Nov 21 16:30:01] 192.168.1.100 - - [21/Nov/2024:16:30:01 +0000] "GET /index.html HTTP/1.1" 200 1024 "-" "Mozilla/5.0"
[Nov 21 16:30:01] 192.168.1.101 - - [21/Nov/2024:16:30:01 +0000] "GET /index.html HTTP/1.1" 200 1024 "-" "Mozilla/5.0"
[Nov 21 16:30:01] 192.168.1.102 - - [21/Nov/2024:16:30:01 +0000] "GET /index.html HTTP/1.1" 200 1024 "-" "Mozilla/5.0"
[Nov 21 16:30:01] 192.168.1.103 - - [21/Nov/2024:16:30:01 +0000] "GET /index.html HTTP/1.1" 200 1024 "-" "Mozilla/5.0"
Signs:
- High volume of requests to the same endpoint within a short time frame.
- Requests originate from multiple IP addresses, often appearing randomized.
2. Abnormal POST Requests
Attackers may send excessive POST requests to endpoints that require server-side processing, such as login forms or APIs.
Log Example (Access Logs):
[Nov 21 16:31:15] 192.168.1.110 - - [21/Nov/2024:16:31:15 +0000] "POST /login HTTP/1.1" 200 512 "-" "Mozilla/5.0"
[Nov 21 16:31:15] 192.168.1.111 - - [21/Nov/2024:16:31:15 +0000] "POST /login HTTP/1.1" 200 512 "-" "Mozilla/5.0"
[Nov 21 16:31:15] 192.168.1.112 - - [21/Nov/2024:16:31:15 +0000] "POST /login HTTP/1.1" 200 512 "-" "Mozilla/5.0"
Signs:
- A sudden spike in POST requests targeting resource-intensive endpoints.
- Uniform payloads, such as repeated username/password combinations, may indicate automated scripts.
3. Targeting Random Resources
Attackers may use bots to target multiple resources at random to bypass caching mechanisms.
Log Example (Access Logs):
[Nov 21 16:32:10] 192.168.1.120 - - [21/Nov/2024:16:32:10 +0000] "GET /product?id=123 HTTP/1.1" 200 2048 "-" "Mozilla/5.0"
[Nov 21 16:32:10] 192.168.1.121 - - [21/Nov/2024:16:32:10 +0000] "GET /product?id=456 HTTP/1.1" 200 2048 "-" "Mozilla/5.0"
[Nov 21 16:32:10] 192.168.1.122 - - [21/Nov/2024:16:32:10 +0000] "GET /product?id=789 HTTP/1.1" 200 2048 "-" "Mozilla/5.0"
Signs:
- Requests target different resources in rapid succession, such as URLs with query parameters.
- Requests bypass static caching layers, forcing the server to generate a fresh response.
4. Uniform Headers Across Requests
Attackers often use automated tools or bots, resulting in identical HTTP headers across requests.
Log Example (Apache Logs):
[Nov 21 16:33:05] 192.168.1.130 - - [21/Nov/2024:16:33:05 +0000] "GET /about.html HTTP/1.1" 200 1024 "-" "AttackBot/1.0"
[Nov 21 16:33:06] 192.168.1.131 - - [21/Nov/2024:16:33:06 +0000] "GET /contact.html HTTP/1.1" 200 1024 "-" "AttackBot/1.0"
[Nov 21 16:33:07] 192.168.1.132 - - [21/Nov/2024:16:33:07 +0000] "GET /services.html HTTP/1.1" 200 1024 "-" "AttackBot/1.0"
Signs:
- User-Agent or other HTTP headers are identical across multiple requests.
- Headers appear unnatural or associated with known malicious bots.
Mitigating HTTP Flood Attacks with WAF and CDN Technologies
Web Application Firewalls (WAFs) and Content Delivery Networks (CDNs) provide protection against HTTP Flood attacks by filtering malicious traffic, distributing loads, and ensuring the availability of backend servers.
WAFs are effective at identifying and blocking malicious HTTP requests that mimic legitimate traffic. They analyze incoming traffic to detect patterns typical of HTTP Flood attacks, such as repeated requests to the same endpoint, abnormally high request rates, or automated scripts targeting resource-intensive URLs. By enforcing rate-limiting rules and leveraging machine learning, WAFs can distinguish between legitimate users and bots, reducing the impact of attacks and protecting server resources.
Specifically, WAF technologies offer:
- Traffic Filtering: Blocks malicious HTTP requests based on signatures, patterns, or behaviors.
- Rate Limiting: Restricts excessive request rates from individual IPs or subnets.
- Bot Detection: Identifies and blocks automated bots involved in HTTP Flood attacks.
- Custom Rules: Enables tailored protections for specific endpoints, such as login pages or APIs.
CDNs complement WAFs by distributing HTTP traffic across their global network of edge servers, ensuring no single origin server becomes overwhelmed. Acting as a buffer, CDNs absorb large volumes of HTTP requests and cache static content to minimize load on the origin server. Additionally, CDNs filter malicious traffic at the edge, identifying suspicious patterns like random URL requests or excessive query parameters. Together, WAFs and CDNs create a multi-layered defense that ensures legitimate traffic is served uninterrupted while malicious requests are blocked or mitigated.
Specifically, CDN technologies offer:
- Traffic Distribution: Spreads HTTP requests across a global network to balance load.
- Edge Filtering: Blocks suspicious HTTP traffic before it reaches the origin server.
- Rate Control: Enforces limits on excessive request rates at the network edge.
- Dynamic Content Handling: Reduces strain on origin servers by caching static resources and dynamically analyzing query patterns.