Beyond the VPN: How ASN Reputation Analysis Prevents Subscription Stacking and Promo Abuse

Introduction

The digital subscription economy is booming, but with growth comes a new wave of sophisticated fraud. Businesses offering free trials and promotions are increasingly targeted by users who exploit these offers through subscription stacking and promo abuse. These fraudulent activities not only lead to significant revenue loss but also drain marketing budgets and skew user data. While many companies have turned to VPN and proxy detection as a first line of defense, savvy fraudsters are now using more advanced techniques to bypass these measures.

A report by Juniper Research predicts that online payment fraud losses will exceed $343 billion between 2023 and 2027. A significant portion of this is attributed to promo and trial abuse.

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This is where ASN reputation analysis comes in. By looking beyond individual IP addresses and analyzing the reputation of the entire network they belong to, businesses can gain a much deeper understanding of a user's digital identity. This article will explore how ASN reputation analysis provides a more robust and effective way to prevent subscription stacking and promo abuse, going beyond the limitations of traditional VPN detection.

We will delve into what Autonomous System Numbers (ASNs) are, how their reputation is determined, and how you can implement this powerful fraud prevention technique. We will also discuss real-world scenarios, common challenges, and best practices to help you build a more secure and resilient business.

The Multi-Billion Dollar Problem of Promo and Subscription Abuse

Promotional abuse, often called Promo Abuse, is a type of fraud where a user signs up for a service multiple times to take advantage of a free trial or promotional offer. This can be done manually or through automated bots, and it has become a massive problem for businesses across various industries, from SaaS to e-commerce. The financial impact of this abuse is staggering, with some estimates suggesting that it costs businesses billions of dollars annually.

The problem is particularly acute for companies that rely on a freemium or trial-based model to attract new customers. While these offers are great for legitimate users who want to try before they buy, they are also a magnet for fraudsters. These individuals have no intention of ever becoming paying customers and are only interested in exploiting the system for their own gain.

This not only leads to a direct loss of potential revenue but also has a number of other negative consequences. For example, it can skew marketing analytics, making it difficult to accurately measure campaign performance and customer acquisition costs. It can also lead to an increase in chargebacks and customer support tickets, further draining a company's resources.

The methods used for promo abuse are constantly evolving. While simple techniques like using disposable email addresses and clearing browser cookies are still common, more sophisticated fraudsters are now using a combination of tools to bypass traditional fraud detection systems. This includes using residential proxies, and even compromised user accounts, to make their fraudulent activity appear legitimate.

Why VPN Detection Is No Longer Enough

For a long time, VPN and proxy detection have been the go-to solutions for businesses looking to prevent promo abuse. The logic is simple: if a user is trying to hide their real IP address, they are likely up to no good. While this approach can be effective at catching unsophisticated fraudsters, it is no longer sufficient to stop more determined attackers.

The main problem is that the landscape of online anonymity has changed. In the past, VPNs were primarily used by a small subset of tech-savvy individuals. Today, they are mainstream tools used by millions of people for legitimate reasons, such as protecting their privacy or accessing geo-restricted content. This means that blocking all VPN users is no longer a viable option, as it would result in a high number of false positives and a poor user experience.

Moreover, fraudsters are no longer limited to using traditional VPNs. They now have access to a wide range of tools that are much harder to detect, such as:

• Residential Proxies: These are IP addresses assigned to real residential devices, making them appear as legitimate users.
• Mobile Proxies: These use mobile data connections to provide a constantly changing IP address, making them difficult to track.
• iCloud Private Relay: This is a new feature from Apple that masks a user's IP address, making it difficult to determine their true location.

As a result of these advancements, businesses that rely solely on VPN detection are finding themselves increasingly vulnerable to promo abuse. A more sophisticated approach is needed, one that looks beyond individual IP addresses and analyzes the broader context of a user's connection.

ASN 101: Understanding the Building Blocks of the Internet

To understand how ASN reputation analysis works, it is first necessary to understand what an Autonomous System Number (ASN) is. In simple terms, an ASN is a unique number that is assigned to a large network of IP addresses, such as an Internet Service Provider (ISP) or a large hosting company. These networks are the building blocks of the internet, and every IP address belongs to an ASN.

You can think of an ASN as a digital neighborhood. Just like a physical neighborhood, a digital neighborhood can have a certain reputation. Some are known for being safe and reliable, while others are known for being hotbeds of criminal activity. By analyzing the reputation of the ASN that a user is connecting from, you can get a good idea of whether they are likely to be a legitimate user or a fraudster.

The reputation of an ASN is determined by a number of factors, including:

• The type of organization that owns the ASN: Is it a well-known ISP or a shady hosting provider that is known for selling bulletproof hosting to criminals?
• The history of the ASN: Has it been associated with a high volume of fraudulent activity in the past?
• The types of IP addresses that are assigned to the ASN: Does it primarily consist of residential IP addresses, or is it made up of a mix of residential, commercial, and data center IPs?

By analyzing these factors, it is possible to assign a reputation score to each ASN. This score can then be used to make a more informed decision about whether to trust a user.

How ASN Reputation Analysis Unmasks Sophisticated Fraudsters

ASN reputation analysis provides a more holistic view of a user's digital identity, making it much more effective at unmasking sophisticated fraudsters. Instead of just looking at a single data point, such as whether a user is using a VPN, it takes into account a wide range of signals to determine the likelihood of fraud. This multi-layered approach makes it much more difficult for fraudsters to bypass.

For example, a user might be connecting from a residential IP address that is not on any blacklist. However, if that IP address belongs to an ASN that has a history of being used for fraud, it is a strong indication that the user may be a fraudster. This is because fraudsters often use a technique called "network hopping," where they constantly switch between different IP addresses within the same ASN to avoid detection.

Another way that ASN reputation analysis can help to unmask fraudsters is by identifying the use of "bulletproof" hosting providers. These are hosting companies that specialize in providing services to criminals and are known for turning a blind eye to fraudulent activity. By blocking an entire ASN that belongs to a bulletproof hosting provider, you can prevent a large number of fraudsters from accessing your service.

Greip's Network Intelligence (ASN) service provides a comprehensive solution for ASN reputation analysis. It allows you to get detailed information about any ASN, including its reputation score, the type of organization that owns it, and its history of fraudulent activity. This information can then be used to create more effective fraud prevention rules.

A Step-by-Step Guide to Implementing ASN Reputation Analysis

Implementing ASN reputation analysis may sound complex, but it can be broken down into a few simple steps. Here is a step-by-step guide to help you get started:

1. Choose an ASN Reputation Provider: The first step is to choose a provider that can give you access to accurate and up-to-date ASN reputation data. There are a number of different providers to choose from, each with its own strengths and weaknesses. Be sure to do your research and choose a provider that is a good fit for your business.
2. Integrate the ASN Reputation API: Once you have chosen a provider, you will need to integrate their API into your fraud prevention workflow. This will allow you to get the reputation score for each user in real-time. Greip's Network Intelligence (ASN) API is a great option for this, as it is easy to integrate and provides a wealth of information about each ASN.
3. Create Custom Fraud Prevention Rules: The next step is to create custom fraud prevention rules based on the ASN reputation score. For example, you might decide to block all users who are connecting from an ASN with a very low reputation score. You could also choose to flag users for manual review if they are connecting from an ASN that has a history of fraudulent activity.
4. Monitor and Adjust Your Rules: It is important to continuously monitor the performance of your fraud prevention rules and make adjustments as needed. This will help you to stay one step ahead of the fraudsters and ensure that you are not blocking legitimate users.

By following these steps, you can implement a robust ASN reputation analysis system that will help you to prevent subscription stacking and promo abuse.

Real-World Scenarios: ASN Reputation in Action

To better understand the power of ASN reputation analysis, let's consider a couple of real-world scenarios.

Scenario 1: The Savvy SaaS Abuser

A user signs up for a free trial of your SaaS product using a disposable email address and a residential proxy. Your traditional VPN & Proxy Detection system does not flag the user as fraudulent because the IP address appears to be legitimate. However, your ASN reputation analysis system notices that the IP address belongs to an ASN that is known for being used by fraudsters.

This raises a red flag, and you decide to take a closer look. You find that the user has signed up for multiple free trials in the past using different IP addresses from the same ASN. This is a clear case of subscription stacking, and you are able to block the user before they can cause any more damage.

Scenario 2: The Coordinated Promo Abuse Attack

A group of fraudsters launches a coordinated attack on your e-commerce store, using a botnet to create thousands of fake accounts and take advantage of a promotional offer. The botnet is made up of a large number of compromised devices, each with a unique IP address. This makes it difficult to detect the attack using traditional fraud prevention methods.

However, your ASN reputation analysis system is able to identify that a large number of the IP addresses are coming from a small number of ASNs that have a very low reputation score. This allows you to quickly identify the attack and block the fraudulent accounts before they can complete their purchases.

These are just two examples of how ASN reputation analysis can be used to prevent promo abuse. By looking beyond individual IP addresses and analyzing the reputation of the entire network, you can gain a much deeper understanding of a user's digital identity and make more informed decisions about who to trust.

Overcoming the Hurdles: Common Challenges in ASN Analysis

While ASN reputation analysis is a powerful tool, it is not without its challenges. Here are a few of the most common hurdles that you may encounter, and how to overcome them:

• False Positives: One of the biggest challenges with any fraud prevention system is the risk of false positives. This is when a legitimate user is incorrectly flagged as a fraudster. To minimize the risk of false positives, it is important to use a high-quality ASN reputation provider and to create custom fraud prevention rules that are tailored to your business.
• Data Accuracy: The accuracy of ASN reputation data can vary from one provider to another. It is important to choose a provider that has a proven track record of providing accurate and up-to-date data. You can also use a combination of different data sources to get a more complete picture of a user's digital identity.
• Evolving Threats: The world of online fraud is constantly evolving, and new threats are emerging all the time. It is important to stay up-to-date on the latest trends and to continuously monitor and adjust your fraud prevention rules as needed. This will help you to stay one step ahead of the fraudsters. For more on this, check out our article on ASN Reputation vs. IP Blacklisting.

By being aware of these challenges and taking steps to overcome them, you can ensure that your ASN reputation analysis system is as effective as possible.

The Future is Proactive: Industry Trends in Fraud Prevention

The fraud prevention industry is constantly evolving, with new technologies and techniques emerging all the time. One of the biggest trends that we are seeing is a shift a more proactive approach to fraud prevention. Instead of just reacting to fraud after it has happened, businesses are now looking for ways to identify and stop fraudsters before they can cause any damage.

ASN reputation analysis is a key part of this proactive approach. By analyzing the reputation of the network that a user is connecting from, you can identify high-risk users before they even have a chance to create an account or make a purchase. This allows you to stop fraud at the source, rather than just dealing with the consequences.

Another major trend is the use of artificial intelligence (AI) and machine learning (ML) to detect and prevent fraud. These technologies can be used to analyze large amounts of data and identify patterns that would be impossible for a human to detect. This can help to improve the accuracy of your fraud prevention system and reduce the number of false positives.

As we look to the future, it is clear that a multi-layered approach to fraud prevention will be essential. This includes a combination of traditional techniques, such as VPN detection, as well as more advanced techniques, such as ASN reputation analysis and AI/ML. By using a combination of these tools, you can build a more resilient and secure business.

Conclusion

The fight against subscription stacking and promo abuse is an ongoing battle, and fraudsters are constantly finding new ways to exploit the system. While traditional methods like VPN detection still have a role to play, they are no longer enough to stop more sophisticated attacks. ASN reputation analysis provides a more robust and effective way to prevent fraud by looking beyond individual IP addresses and analyzing the reputation of the entire network.

By implementing an ASN reputation analysis system, you can:

• Unmask sophisticated fraudsters who are using advanced techniques to bypass traditional fraud detection systems.
• Prevent subscription stacking and promo abuse by identifying high-risk users before they can cause any damage.
• Reduce revenue loss and protect your marketing budget from being drained by fraudulent activity.
• Improve the accuracy of your marketing analytics by filtering out fake users and bots.

If you are looking for a powerful and effective way to prevent promo abuse, then ASN reputation analysis is a great place to start. And with Greip's ASN Lookup tool and Network Intelligence API, you have everything you need to get started.