What HTTP flood attack is?

HTTP flood attack is one of the most prevalent and challenging forms of Distributed Denial of Service (DDoS) attacks targeting the application layer (Layer 7 in the OSI model). These attacks focus on overwhelming a target server by flooding it with HTTP requests, exploiting the protocols and frameworks that web applications rely upon. Unlike other types of DDoS attacks that aim at exhausting network bandwidth or transport protocols (such as SYN floods), HTTP flood attacks target the application layer, making them both stealthier and harder to detect and mitigate.

This article delves into the intricacies of DDoS HTTP flood attacks, exploring their types, mechanisms, impact, detection techniques, and mitigation strategies.

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What is an HTTP Flood Attack?

An HTTP flood attack leverages HTTP requests to overwhelm a targeted server or application, aiming to exhaust its resources (such as CPU and memory) and degrade or halt its services. Since HTTP is a high-level protocol, it involves more processing power than lower-level protocols, making it particularly effective for attackers trying to achieve maximum impact with fewer resources.

Unlike volumetric attacks that attempt to saturate a network with sheer bandwidth, HTTP flood attacks do not require massive amounts of traffic to be effective. Instead, they focus on exploiting legitimate application functionalities, making them harder to distinguish from normal traffic.

How Does an HTTP Flood Attack Work?

The general mechanism of an HTTP flood attack involves sending a large number of seemingly legitimate HTTP requests to a web server. These requests can range from simple GET requests (fetching pages or files) to complex POST requests (submitting forms, logging in, uploading data). These requests typically fall into two main categories:

1. HTTP GET Flood: In a GET flood, the attacker continuously sends GET requests to retrieve resources (web pages, images, files) from the server. While each individual GET request is relatively light, a high volume of these requests can lead to resource exhaustion. Attackers often target resource-intensive URLs (such as dynamic pages or large files) to maximize impact.
2. HTTP POST Flood: POST floods are more complex and potentially more damaging than GET floods. In a POST flood, the attacker sends POST requests containing data (such as form submissions or login attempts) that require backend processing. This processing is often CPU- and memory-intensive, which makes it easier to exhaust server resources and slow down the application for legitimate users.

The primary goal of an HTTP flood attack is to consume server resources, thus causing increased latency, timeouts, or complete service unavailability for legitimate users.

Types of HTTP Flood Attacks

HTTP flood attacks can be classified based on the complexity and characteristics of the requests being sent. The most common types are:

1. Basic HTTP Flood:
   • This is the simplest form of HTTP flood, where attackers send repeated GET or POST requests to a single URL or set of URLs.
   • These requests often originate from a botnet, a network of compromised devices controlled by an attacker.
2. Randomized HTTP Flood:
   • In this type, the attacker randomizes the URLs being requested, making it harder to detect the attack by analyzing request patterns.
   • Attackers might add query parameters to URLs or use randomized headers to mimic legitimate traffic, increasing the difficulty of detection.
3. Browser-Based HTTP Flood:
   • Some attackers use scripts within browsers or even headless browsers (e.g., PhantomJS) to simulate legitimate user behavior.
   • By imitating real browser sessions (with cookies, headers, and referrer data), attackers make the traffic appear more authentic.
4. Slowloris or Slow HTTP Attack:
   • Although not a traditional flood, Slowloris attacks can create similar effects. This involves sending HTTP headers very slowly to keep the connection open for extended periods, consuming server resources and preventing legitimate connections.
   • Slow HTTP attacks are particularly damaging to servers with limited connection handling capacity.

Mechanisms and Tools for Executing HTTP Attacks

Attackers employ various tools and mechanisms to execute HTTP flood attacks. Some commonly used tools include:

• LOIC (Low Orbit Ion Cannon): Originally designed for network stress testing, LOIC is often repurposed for DDoS attacks, including HTTP floods.
• HOIC (High Orbit Ion Cannon): An upgrade of LOIC, HOIC allows attackers to send a high volume of randomized HTTP requests, making it effective for HTTP flood attacks.
• HTTP Unbearable Load King (HULK): A more sophisticated tool, HULK generates randomized URLs and headers, making the requests appear unique and harder to filter.

Attackers may also use botnets and rented DDoS services to launch HTTP flood attacks, significantly amplifying their impact and bypassing IP-based blocking measures.

Impact of HTTP Flood Attacks

The impact of an HTTP flood attack can be severe, especially for businesses reliant on web applications or services. Some of the primary consequences include:

• Increased Latency: Due to the high volume of requests, response times slow down, leading to poor user experience.
• Resource Exhaustion: HTTP flood attacks strain CPU, memory, and database resources, potentially causing the server to crash.
• Service Unavailability: In severe cases, HTTP floods can completely overwhelm the application, leading to downtime and loss of business.
• Revenue Loss: For e-commerce websites, even minor downtime can lead to significant revenue losses due to missed transactions.

Detection of HTTP Flood Attacks

Detecting HTTP flood attacks is challenging due to their similarity to legitimate traffic. However, certain indicators can help identify such attacks:

1. Spike in Traffic with Uniform Patterns: A sudden and sustained increase in HTTP requests, especially if directed at a few specific URLs, can indicate an attack.
2. Increased Server Load with High HTTP Connection Rates: If the server CPU usage is consistently high with a large number of simultaneous HTTP connections, this may suggest a flood attack.
3. Unusual User Agent Patterns: Many HTTP flood attacks use botnets or scripted browsers with default or fake user-agent headers, which can be a red flag.
4. Unexpectedly High POST Requests: A spike in POST requests, especially with similar payloads, could indicate a targeted POST flood attack.

Most HTTP flood detection mechanisms use behavioral analysis and anomaly detection to differentiate between legitimate and malicious traffic patterns. Advanced detection tools may incorporate machine learning models trained on normal traffic patterns to identify deviations indicating an attack.

Mitigating HTTP Flood Attacks

Mitigating HTTP flood attacks requires a combination of techniques at the network, application, and server levels:

1. Rate Limiting:
   • Limiting the rate of requests per IP address or per session can mitigate the impact of an HTTP flood.
   • However, this is effective mainly against non-distributed attacks, as distributed botnets can bypass simple rate limits.
2. IP Reputation and Blacklisting:
   • Using IP reputation services can help filter out known malicious IPs.
   • Dynamic blacklisting of IPs showing abnormal behavior can be useful, though sophisticated attacks may use frequently changing IPs or proxies.
3. CAPTCHAs and JavaScript Challenges:
   • CAPTCHAs and JavaScript challenges can filter out bots by requiring user interaction.
   • Browser-based HTTP flood attacks that do not interact with the CAPTCHA will be blocked, while legitimate users can proceed.
4. Web Application Firewalls (WAFs):
   • WAFs are essential tools for application-layer DDoS protection, as they can inspect HTTP requests for malicious patterns and filter them.
   • Modern WAFs with machine learning capabilities can adapt to detect and block suspicious patterns specific to HTTP flood attacks.
5. Traffic Shaping and Load Balancing:
   • Load balancers and traffic shapers can distribute incoming traffic across multiple servers, reducing the impact on any single server.
   • This also provides redundancy in case one server becomes overloaded.
6. Behavioral Analysis and Anomaly Detection:
   • Advanced DDoS protection services use machine learning algorithms to analyze traffic patterns, identifying deviations indicative of an attack.
   • By continuously adapting to normal traffic patterns, these solutions can more accurately distinguish between legitimate traffic and HTTP flood attacks.

Conclusion

HTTP flood attacks represent a sophisticated and potentially devastating method of DDoS, exploiting the legitimate functionalities of web applications to degrade or halt services. Understanding their mechanisms and the methods to detect and mitigate them is crucial for maintaining application uptime and protecting business operations. By combining rate limiting, WAFs, IP reputation filtering, and behavioral analysis, organizations can effectively reduce the risk and impact of HTTP flood attacks, ensuring resilient and responsive web applications.