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Break the DDoS Attack Loop With Rate Limiting

This Tech Tip demonstrates how security engineers can best use rate limits to mitigate distributed denial-of-service attacks.

Venkatesh Sundar

May 15, 2023

4 Min Read
Illustration of a bank of servers shielded from a hoodie-wearing hacker at a desktop PC
Source: Golden Sikorka via Adobe Stock

Distributed denial-of-service (DDoS) attacks are growing in frequency and sophistication, thanks to the number of attack tools available for a couple of dollars on the Dark Web and criminal marketplaces. Numerous organizations became victims in 2022, from the Port of London Authority to Ukraine's national postal service.

Security leaders are already combating DDoS attacks by monitoring network traffic patterns, implementing firewalls, and using content delivery networks (CDNs) to distribute traffic across multiple servers. But putting more security controls in place can also result in more DDoS false positives — legitimate traffic that's not part of an attack but still requires analysts to take steps to mitigate before it causes service disruptions and brand damage.

Rate limiting is often considered the best method for efficient DDoS mitigation: URL-specific rate limiting prevents 47% of DDoS attacks, according to Indusface's "State of Application Security Q4 2022" report. However, the reality is that few engineering leaders know how to use it effectively. Here's how to employ rate limiting effectively while avoiding false positives.

Understand Expected Network Traffic and Vulnerabilities

Engineering leaders often find it difficult to implement rate limiting as a DDoS mitigation tool because they don't know what thresholds to set. The first step is to answer the following questions:

  • How many users visit your application every minute?

  • How many report/dashboard actions can your application handle? Is that the same for a reset password page?

  • Since server load on dashboards tends to be high, could a lower rate limit block legitimate users trying to access a cheaper resource, such as a profile page, for example?

Going over 100 requests in one minute on a login page could be enough to take the server down, while a product page might have no trouble handling 300 requests in a minute. That's why it is useful to know the threshold of network traffic for each URL within each application.

Network monitoring tools, log files, and buffer capacity can help teams develop accurate baseline network traffic models and manage incoming and outgoing data flow. Suppose you ran a Christmas holiday campaign over 30 days, and the request limit was 300 per minute. To clearly understand the expected network traffic, the security and DevOps teams need to know two things: How many requests were made each minute on average? And if there were 480 requests in one minute, does the team get an alert to check that it was legitimate traffic?

Having granular details on IP, host, domain, and URI vulnerabilities means teams can act more quickly to thwart DDoS attacks.

Numerous security teams have been surprised to receive alerts about attacks targeting their human resource management systems, not just consumer-facing business websites. It is vital to be aware of all the potential applications targeted by DDoS attacks to reduce false alarms.

Implement Custom Rate Limits on Various Parameters

Security teams want around-the-clock application availability and are relying on managed services to get more value from DDoS mitigation software. In-built DDoS scrubbers help security leaders go beyond static rate limits and customize rules based on the behavior of inbound traffic received by host, IP, URL, and geography.

So what should cybersecurity teams know about rate limits?

  • Never do rate limits on the domain level (e.g., acme.com). Hundreds of URLs get added to the domain, which lowers the per-page requests needed to trigger the rate limit. This can cause unnecessary blocking of legitimate requests or, if you compensate by raising the rate limits overall, allow too many malicious requests to pass through.

  • Set rate limits on the URL (e.g., acme.com/login) to control which customers can access a particular URL or set of URLs. Cybersecurity teams can set rate limits differently for each URL, and a server may block requests if the number exceeds the rate limit.

  • Customize the rate of requests on a session level (the time logged in) to detect unusual behavior that may indicate malicious activity and thus prevent servers from being overwhelmed. For example, if a user opens acme.com 100 times a minute, that's not normal behavior.

  • Monitor rate limits at an IP level to limit the number of requests or connections from a particular IP address. IP blacklisting — adding known malicious actors or sources to a blacklist — makes it easier for website owners to block traffic from IP addresses known to be involved in DDoS attacks.

  • Implement geographical rate limiting. Security leaders need to quickly examine IP address reputations and geolocation data to verify the source of traffic. As a best practice, I recommend teams implement geofencing as a standard for all local applications.

By using the above methods, application owners end up setting more granular rate limits by using system recommendations based on user behavior. This — in conjunction with using DDoS mitigation mechanisms, such as tarpitting and CAPTCHA, before blocking requests — can minimize false positives to the maximum extent possible.

Cybersecurity decision-makers must take a multilayered approach to protection by having a clear understanding of network traffic patterns and using fully managed platforms to set rate limits for threat intelligence.

About the Author(s)

Venkatesh Sundar

Founder, Indusface

Sundar runs marketing, inside sales, and sales engineering at Indusface and is responsible for new customer acquisition and global revenue growth. Before taking on the marketing and sales functions at Indusface, Sundar was the founding CTO who built the products and technology team from the ground up. Prior to Indusface, he had 10+ years of experience in cybersecurity and held management roles in engineering, services, and sales in US-based product companies.

He earned his bachelor's degree in computer science from University of Pune and an MBA from Queens University Canada.

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